Amazon SageMaker Developer Guide

Table of contents :
Amazon SageMaker
Table of Contents
What Is Amazon SageMaker?
Amazon SageMaker Features
Amazon SageMaker Pricing
Are You a First-time User of Amazon SageMaker?
How Amazon SageMaker Works
Machine Learning with Amazon SageMaker
Explore, Analyze, and Process Data
What Is Fairness and Model Explainability for Machine Learning Predictions?
Best Practices for Evaluating Fairness and Explainability in the ML Lifecycle
Sample Notebooks
Guide to the SageMaker Clarify Documentation
Train a Model with Amazon SageMaker
Deploy a Model in Amazon SageMaker
Deploy a Model on SageMaker Hosting Services
Best Practices for Deploying Models on SageMaker Hosting Services
Get Inferences for an Entire Dataset with Batch Transform
Validate a Machine Learning Model
Monitoring a Model in Production
Use Machine Learning Frameworks, Python, and R with Amazon SageMaker
Use Apache MXNet with Amazon SageMaker
What do you want to do?
Use Apache Spark with Amazon SageMaker
Download the SageMaker Spark Library
Integrate Your Apache Spark Application with SageMaker
Example 1: Use Amazon SageMaker for Training and Inference with Apache Spark
Use Custom Algorithms for Model Training and Hosting on Amazon SageMaker with Apache Spark
Use the SageMakerEstimator in a Spark Pipeline
SDK examples: Use Amazon SageMaker with Apache Spark
Use Chainer with Amazon SageMaker
What do you want to do?
Use Hugging Face with Amazon SageMaker
Training
How to run training with the Hugging Face Estimator
Inference
How to deploy an inference job using the Hugging Face Deep Learning Containers
What do you want to do?
Use PyTorch with Amazon SageMaker
What do you want to do?
R User Guide to Amazon SageMaker
R Kernel in SageMaker
Get Started with R in SageMaker
Example Notebooks
Use Scikit-learn with Amazon SageMaker
What do you want to do?
Use SparkML Serving with Amazon SageMaker
Use TensorFlow with Amazon SageMaker
Use TensorFlow Version 1.11 and Later
What do you want to do?
Use TensorFlow Legacy Mode for Versions 1.11 and Earlier
Supported Regions and Quotas
Request a service quota increase for SageMaker resources
Get Started with Amazon SageMaker
Set Up Amazon SageMaker
Create an AWS Account
Create an IAM Administrator User and Group
Onboard to Amazon SageMaker Studio
Onboard to Amazon SageMaker Studio Using Quick Start
Onboard to Amazon SageMaker Studio Using AWS SSO
Set Up AWS SSO for Use with Amazon SageMaker Studio
Onboard to Amazon SageMaker Studio Using IAM
Choose a VPC
Delete an Amazon SageMaker Studio Domain
Delete a SageMaker Studio Domain (Studio)
Delete a SageMaker Studio Domain (CLI)
SageMaker JumpStart
Using JumpStart
Solutions
Models
Text Models
Vision Models
Deploy a model
Model Deployment Configuration
Fine-Tune a Model
Fine-Tuning Data Source
Fine-Tuning deployment configuration
Hyperparameters
Training Output
Next Steps
Amazon SageMaker Studio Tour
Get Started with Amazon SageMaker Notebook Instances
Machine Learning with the SageMaker Python SDK
Tutorial Overview
Step 1: Create an Amazon SageMaker Notebook Instance
(Optional) Change SageMaker Notebook Instance Settings
(Optional) Advanced Settings for SageMaker Notebook Instances
Step 2: Create a Jupyter Notebook
Step 3: Download, Explore, and Transform a Dataset
Load Adult Census Dataset Using SHAP
Overview the Dataset
Split the Dataset into Train, Validation, and Test Datasets
Convert the Train and Validation Datasets to CSV Files
Upload the Datasets to Amazon S3
Step 4: Train a Model
Choose the Training Algorithm
Create and Run a Training Job
Step 5: Deploy the Model to Amazon EC2
Deploy the Model to SageMaker Hosting Services
(Optional) Use SageMaker Predictor to Reuse the Hosted Endpoint
(Optional) Make Prediction with Batch Transform
Step 6: Evaluate the Model
Evaluate the Model Deployed to SageMaker Hosting Services
Step 7: Clean Up
Amazon SageMaker Studio
Amazon SageMaker Studio UI Overview
Left sidebar
File and resource browser
Main work area
Settings
Use the Amazon SageMaker Studio Launcher
Notebooks and compute resources
Utilities and files
Studio Entity Status
Use Amazon SageMaker Studio Notebooks
How Are Amazon SageMaker Studio Notebooks Different from Notebook Instances?
Get Started
Log In from the Amazon SageMaker console
Next Steps
Create or Open an Amazon SageMaker Studio Notebook
Open a Studio notebook
Create a Notebook from the File Menu
Create a Notebook from the Launcher
Use the SageMaker Studio Notebook Toolbar
Share and Use an Amazon SageMaker Studio Notebook
Share a Notebook
Use a Shared Notebook
Get Notebook and App Metadata
Get Notebook Metadata
Get App Metadata
Get Notebook Differences
Get the Difference Between the Last Checkpoint
Get the Difference Between the Last Commit
Manage Resources
Change an Instance Type
Change a Kernel
Shut Down Resources
Shut Down an Open Notebook
Shut Down Resources
Usage Metering
Available Resources
Available SageMaker Studio Instance Types
Available Amazon SageMaker Images
Available Amazon SageMaker Kernels
Bring your own SageMaker image
Create a custom SageMaker image (Console)
Attach a custom SageMaker image (Control Panel)
Attach an existing image version to your domain
Detach a custom SageMaker image
Launch a custom SageMaker image in SageMaker Studio
Bring your own custom SageMaker image tutorial
Add a Studio-compatible container image to Amazon ECR
Create a SageMaker image from the ECR container image
Attach the SageMaker image to a new domain
Attach the SageMaker image to your current domain
View the attached image in the Studio control panel
Clean up resources
Custom SageMaker image specifications
Set Up a Connection to an Amazon EMR Cluster
Perform Common Tasks in Amazon SageMaker Studio
Upload Files to SageMaker Studio
Clone a Git Repository in SageMaker Studio
Stop a Training Job in SageMaker Studio
Use TensorBoard in Amazon SageMaker Studio
Prerequisites
Set Up TensorBoardCallback
Install TensorBoard
Launch TensorBoard
Manage Your EFS Storage Volume in SageMaker Studio
Provide Feedback on SageMaker Studio
Update SageMaker Studio and Studio Apps
Update SageMaker Studio
Update Studio Apps
Amazon SageMaker Studio Pricing
Troubleshooting Amazon SageMaker Studio
Use Amazon SageMaker Notebook Instances
Amazon Linux 2 vs Amazon Linux notebook instances
AL1 Maintenance Phase Plan
Available Kernels
Migrating to Amazon Linux 2
Create a Notebook Instance
Access Notebook Instances
Update a Notebook Instance
Customize a Notebook Instance Using a Lifecycle Configuration Script
Lifecycle Configuration Best Practices
Install External Libraries and Kernels in Notebook Instances
Package installation tools
Conda
Pip
Unsupported
Notebook Instance Software Updates
Control an Amazon EMR Spark Instance Using a Notebook
Example Notebooks
Use or View Example Notebooks in Jupyter Classic
Use or View Example Notebooks in Jupyterlab
Set the Notebook Kernel
Associate Git Repositories with SageMaker Notebook Instances
Add a Git Repository to Your Amazon SageMaker Account
Add a Git Repository to Your SageMaker Account (Console)
Add a Git Repository to Your Amazon SageMaker Account (CLI)
Create a Notebook Instance with an Associated Git Repository
Create a Notebook Instance with an Associated Git Repository (Console)
Create a Notebook Instance with an Associated Git Repository (CLI)
Associate a CodeCommit Repository in a Different AWS Account with a Notebook Instance
Use Git Repositories in a Notebook Instance
Notebook Instance Metadata
Monitor Jupyter Logs in Amazon CloudWatch Logs
Automate model development with Amazon SageMaker Autopilot
Get started with Amazon SageMaker Autopilot
Samples: Explore modeling with Amazon SageMaker Autopilot
Videos: Use Autopilot to automate and explore the machine learning process
Start an AutoML job with Amazon SageMaker Autopilot
Review data exploration and feature engineering automated in Autopilot.
Tune models to optimize performance
Choose and deploy the best model
Amazon SageMaker Autopilot walkthrough
Tutorials: Get started with Amazon SageMaker Autopilot
Create an Amazon SageMaker Autopilot experiment
Amazon SageMaker Autopilot problem types
Regression
Binary classification
Multiclass classification
Model support and validation
Autopilot algorithm support
Autopilot cross-validation
Amazon SageMaker Autopilot model deployment
Amazon SageMaker Autopilot explainability
Models generated by Amazon SageMaker Autopilot
Amazon SageMaker Autopilot notebooks generated to manage AutoML tasks
Data exploration notebook
Candidate definition notebook
Configure inference output in Autopilot-generated containers
Inference container definitions for regression and classification problem types
Select inference response for classification models
Amazon SageMaker Autopilot quotas
Quotas that you can increase
Resource quotas
API reference guide for Amazon SageMaker Autopilot
SageMaker API reference
Amazon SageMaker Python SDK
AWS Command Line Interface (CLI)
AWS SDK for Python (Boto)
AWS SDK for .NET
AWS SDK for C++
AWS SDK for Go
AWS SDK for Java
AWS SDK for JavaScript
AWS SDK for PHP V3
AWS SDK for Ruby V3
Label Data
Use Amazon SageMaker Ground Truth to Label Data
Are You a First-time User of Ground Truth?
Getting started
Step 1: Before You Begin
Next
Step 2: Create a Labeling Job
Next
Step 3: Select Workers
Next
Step 4: Configure the Bounding Box Tool
Next
Step 5: Monitoring Your Labeling Job
Label Images
Bounding Box
Creating a Bounding Box Labeling Job (Console)
Create a Bounding Box Labeling Job (API)
Provide a Template for Bounding Box Labeling Jobs
Bounding Box Output Data
Image Semantic Segmentation
Creating a Semantic Segmentation Labeling Job (Console)
Create a Semantic Segmentation Labeling Job (API)
Provide a Template for Semantic Segmentation Labeling Jobs
Semantic Segmentation Output Data
Auto-Segmentation Tool
Tool Preview
Tool Availability
Image Classification (Single Label)
Create an Image Classification Labeling Job (Console)
Create an Image Classification Labeling Job (API)
Provide a Template for Image Classification Labeling Jobs
Image Classification Output Data
Image Classification (Multi-label)
Create a Multi-Label Image Classification Labeling Job (Console)
Create a Multi-Label Image Classification Labeling Job (API)
Provide a Template for Multi-label Image Classification
Multi-label Image Classification Output Data
Image Label Verification
Use Ground Truth to Label Text
Named Entity Recognition
Create a Named Entity Recognition Labeling Job (Console)
Create a Named Entity Recognition Labeling Job (API)
Provide a Template for Named Entity Recognition Labeling Jobs
Named Entity Recognition Output Data
Text Classification (Single Label)
Create a Text Classification Labeling Job (Console)
Create a Text Classification Labeling Job (API)
Provide a Template for Text Classification Labeling Jobs
Text Classification Output Data
Text Classification (Multi-label)
Create a Multi-Label Text Classification Labeling Job (Console)
Create a Multi-Label Text Classification Labeling Job (API)
Create a Template for Multi-label Text Classification
Multi-label Text Classification Output Data
Label Videos and Video Frames
Video Classification
Create a Video Classification Labeling Job (Console)
Create a Video Classification Labeling Job (API)
Provide a Template for Video Classification
Video Classification Output Data
Label Video Frames
Video Frame Object Detection
Preview the Worker UI
Create a Video Frame Object Detection Labeling Job
Create a Labeling Job (Console)
Create a Labeling Job (API)
Create Video Frame Object Detection Adjustment or Verification Labeling Job
Output Data Format
Video Frame Object Tracking
Preview the Worker UI
Create a Video Frame Object Tracking Labeling Job
Create a Labeling Job (Console)
Create a Labeling Job (API)
Create a Video Frame Object Tracking Adjustment or Verification Labeling Job
Output Data Format
Video Frame Labeling Job Overview
Input Data
Job Completion Times
Task Types
Workforces
Worker User Interface (UI)
Label Category and Frame Attributes
Label Category Attributes
Frame level Attributes
Worker Instructions
Declining Tasks
Video Frame Job Permission Requirements
Add a CORS Permission Policy to S3 Bucket
Worker Instructions
Work on Video Frame Object Tracking Tasks
Your Task
Navigate the UI
Bulk Edit Label and Frame Attributes
Tool Guide
Icons Guide
Shortcuts
Release, Stop and Resume, and Decline Tasks
Saving Your Work and Submitting
Work on Video Frame Object Detection Tasks
Your Task
Navigate the UI
Bulk Edit Label and Frame Attributes
Tool Guide
UI Icon Guide
Shortcuts
Release, Stop and Resume, and Decline Tasks
Saving Your Work and Submitting
Use Ground Truth to Label 3D Point Clouds
3D Point Clouds
LiDAR
Sensor Fusion
Label 3D Point Clouds
Assistive Labeling Tools for Point Cloud Annotation
Next Steps
3D Point Cloud Task types
3D Point Cloud Object Detection
View the Worker Task Interface
Create a 3D Point Cloud Object Detection Labeling Job
Create a Labeling Job (Console)
Create a Labeling Job (API)
Create a 3D Point Cloud Object Detection Adjustment or Verification Labeling Job
Output Data Format
3D Point Cloud Object Tracking
View the Worker Task Interface
Worker Tools
Create a 3D Point Cloud Object Tracking Labeling Job
Create a Labeling Job (API)
Create a Labeling Job (Console)
Create a 3D Point Cloud Object Tracking Adjustment or Verification Labeling Job
Output Data Format
3D Point Cloud Semantic Segmentation
View the Worker Task Interface
Create a 3D Point Cloud Semantic Segmentation Labeling Job
Create a Labeling Job (Console)
Create a Labeling Job (API)
Create a 3D Point Cloud Semantic Segmentation Adjustment or Verification Labeling Job
Output Data Format
3D Point Cloud Labeling Jobs Overview
Job Pre-processing Time
Job Completion Times
Workforces
Worker User Interface (UI)
Label Category Attributes
Label Category Attributes
Frame Attributes
Worker Instructions
Declining Tasks
3D Point Cloud Labeling Job Permission Requirements
Add a CORS Permission Policy to S3 Bucket
Worker Instructions
3D Point Cloud Semantic Segmentation
Your Task
Navigate the UI
Icon Guide
Shortcuts
Release, Stop and Resume, and Decline Tasks
Saving Your Work and Submitting
3D Point Cloud Object Detection
Your Task
Navigate the UI
Icon Guide
Shortcuts
Release, Stop and Resume, and Decline Tasks
Saving Your Work and Submitting
3D Point Cloud Object Tracking
Your Task
Navigate the UI
Delete Cuboids
Bulk Edit Label Category and Frame Attributes
Icon Guide
Shortcuts
Release, Stop and Resume, and Decline Tasks
Saving Your Work and Submitting
Verify and Adjust Labels
Requirements to Create Verification and Adjustment Labeling Jobs
Create a Label Verification Job (Console)
Create an Image Label Verification Job (Console)
Create a Point Cloud or Video Frame Label Verification Job (Console)
Create a Label Adjustment Job (Console)
Create an Image Label Adjustment Job (Console)
Create a Point Cloud or Video Frame Label Adjustment Job (Console)
Start a Label Verification or Adjustment Job (API)
Bounding Box and Semantic Segmentation
3D Point Cloud and Video Frame
Label Verification and Adjustment Data in the Output Manifest
Cautions and Considerations
Color Information Requirements for Semantic Segmentation Jobs
Filter Your Data Before Starting the Job
Creating Custom Labeling Workflows
Step 1: Setting up your workforce
Next
Step 2: Creating your custom worker task template
Starting with a base template
Developing templates locally
Using External Assets
Track your variables
A simple sample
Adding automation with Liquid
Variable filters
Autoescape and explicit escape
escape_once
skip_autoescape
to_json
grant_read_access
End-to-end demos
Next
Step 3: Processing with AWS Lambda
Pre-annotation and Post-annotation Lambda Function Requirements
Pre-annotation Lambda
Examples of Pre-annotation Lambda Functions
Post-annotation Lambda
Required Permissions To Use AWS Lambda With Ground Truth
Grant Permission to Create and Select an AWS Lambda Function
Grant IAM Execution Role Permission to Invoke AWS Lambda Functions
Grant Post-Annotation Lambda Permissions to Access Annotation
Create Lambda Functions for a Custom Labeling Workflow
Test Pre-Annotation and Post-Annotation Lambda Functions
Prerequisites
Test the Pre-annotation Lambda Function
Test the Post-Annotation Lambda Function
Demo Template: Annotation of Images with crowd-bounding-box
Starter Bounding Box custom template
Your own Bounding Box custom template
Your manifest file
Your pre-annotation Lambda function
Your post-annotation Lambda function
The output of your labeling job
Demo Template: Labeling Intents with crowd-classifier
Starter Intent Detection custom template
Your Intent Detection custom template
Styling Your Elements
Your pre-annotation Lambda function
Your post-annotation Lambda function
Your labeling job output
Custom Workflows via the API
Create a Labeling Job
Built-in Task Types
Creating Instruction Pages
Short Instructions
Full Instructions
Add example images to your instructions
Create a Labeling Job (Console)
Next Steps
Create a Labeling Job (API)
Examples
Create a Streaming Labeling Job
Create Amazon SNS Input and Output Topics
Create an Input Topic
Create an Output Topic
Add Encryption to Your Output Topic (Optional)
Subscribe an Endpoint to Your Amazon SNS Output Topic
Set up Amazon S3 Bucket Event Notifications
Create a Manifest File (Optional)
Example: Use SageMaker API To Create Streaming Labeling Job
Stop a Streaming Labeling Job
Create a Labeling Category Configuration File with Label Category and Frame Attributes
Label Category Configuration File Schema
Label and label category attribute quotas
Example: Label Category Configuration Files for 3D Point Cloud Labeling Jobs
Example: Label Category Configuration Files for Video Frame Labeling Jobs
Creating Worker Instructions
Use Input and Output Data
Input Data
Use an Input Manifest File
Automated Data Setup
Supported Data Formats
Ground Truth Streaming Labeling Jobs
How It Works
Send Data to a Streaming Labeling Job
Send Data Objects Using Amazon SNS
Send Data Objects using Amazon S3
Manage Labeling Requests with an Amazon SQS Queue
Receive Output Data from a Streaming Labeling Job
Duplicate Message Handling
Specify A Deduplication Key and ID in an Amazon SNS Message
Find Deduplication Key and ID in Your Output Data
Input Data Quotas
Input File Size Quota
Input Image Resolution Quotas
Label Category Quotas
3D Point Cloud and Video Frame Labeling Job Quotas
Filter and Select Data for Labeling
Use the Full Dataset
Choose a Random Sample
Specify a Subset
3D Point Cloud Input Data
Accepted Raw 3D Data Formats
Compact Binary Pack Format
ASCII Format
Point Cloud Resolution Limits
Create an Input Manifest File for a 3D Point Cloud Labeling Job
Create a Point Cloud Frame Input Manifest File
Include Vehicle Pose Information in Your Input Manifest
Include Camera Data in Your Input Manifest
Point Cloud Frame Limits
Create a Point Cloud Sequence Input Manifest
Parameters for Individual Point Cloud Frames
Include Vehicle Pose Information in Your Input Manifest
Include Camera Data in Your Input Manifest
Sequence File and Point Cloud Frame Limits
Understand Coordinate Systems and Sensor Fusion
Coordinate System Requirements for Labeling Jobs
Using Point Cloud Data in a World Coordinate System
What is a World Coordinate System?
Convert 3D Point Cloud Data to a WCS
Sensor Fusion
Extrinsic Matrix
Intrinsic Matrix
Image Distortion
Ego Vehicle
Pose
Compute Orientation Quaternions and Position
Ground Truth Sensor Fusion Transformations
LiDAR Extrinsic
Camera Calibrations: Extrinsic, Intrinsic and Distortion
Camera Extrinsic
Intrinsic and Distortion
Video Frame Input Data
Choose Video Files or Video Frames for Input Data
Provide Video Frames
Provide Video Files
Input Data Setup
Automated Video Frame Input Data Setup
Provide Video Files and Extract Frames
Provide Video Frames
Manual Input Data Setup
Create a Video Frame Input Manifest File
Create a Video Frame Sequence Input Manifest
Create a Video Frame Sequence File
Output Data
Output Directories
Active Learning Directory
Annotations Directory
Inference Directory
Manifest Directory
Training Directory
Confidence Score
Worker Metadata
Output Metadata
Classification Job Output
Multi-label Classification Job Output
Bounding Box Job Output
Named Entity Recognition
Label Verification Job Output
Semantic Segmentation Job Output
Video Frame Object Detection Output
Video Frame Object Tracking Output
3D Point Cloud Semantic Segmentation Output
3D Point Cloud Object Detection Output
3D Point Cloud Object Tracking Output
Enhanced Data Labeling
Control the Flow of Data Objects Sent to Workers
Use MaxConcurrentTaskCount to Control the Flow of Data Objects
Use Amazon SQS to Control the Flow of Data Objects to Streaming Labeling Jobs
Consolidate Annotations
Create Your Own Annotation Consolidation Function
Assess Similarity
Assess the Most Probable Label
Automate Data Labeling
How it Works
Accuracy of Automated Labels
Create an Automated Data Labeling Job (Console)
Create an Automated Data Labeling Job (API)
Amazon EC2 Instances Required for Automated Data Labeling
Set up an active learning workflow with your own model
Chaining Labeling Jobs
Key Term: Label Attribute Name
Start a Chained Job (Console)
Job Overview Panel
Start a Chained Job (API)
Use a Partially Labeled Dataset
Ground Truth Security and Permissions
CORS Permission Requirement
Assign IAM Permissions to Use Ground Truth
Use IAM Managed Policies with Ground Truth
Grant IAM Permission to Use the Amazon SageMaker Ground Truth Console
Ground Truth Console Permissions
Custom Labeling Workflow Permissions
Private Workforce Permissions
Vendor Workforce Permissions
Create a SageMaker Execution Role for a Ground Truth Labeling Job
Built-In Task Types (Non-streaming) Execution Role Requirements
Built-In Task Types (Streaming) Execution Role Requirements
Execution Role Requirements for Custom Task Types
Automated Data Labeling Permission Requirements
Encrypt Output Data and Storage Volume with AWS KMS
Encrypt Output Data using KMS
Encrypt Automated Data Labeling ML Compute Instance Storage Volume
Output Data and Storage Volume Encryption
Use Your KMS Key to Encrypt Output Data
Use Your KMS Key to Encrypt Automated Data Labeling Storage Volume (API Only)
Workforce Authentication and Restrictions
Restrict Access to Workforce Types
Monitor Labeling Job Status
Send Events to CloudWatch Events
Set Up a Target to Process Events
Labeling Job Expiration
Declining Tasks
Create and Manage Workforces
Using the Amazon Mechanical Turk Workforce
Use Mechanical Turk with Ground Truth
Use Mechanical Turk with Amazon A2I
When is Mechanical Turk Not Supported?
Managing Vendor Workforces
Use a Private Workforce
Create and Manage Amazon Cognito Workforce
Create a Private Workforce (Amazon Cognito)
Create a Private Workforce (Amazon SageMaker Console)
Create an Amazon Cognito Workforce When Creating a Labeling Job
Create an Amazon Cognito Workforce Using the Labeling Workforces Page
Create a Private Workforce (Amazon Cognito Console)
Manage a Private Workforce (Amazon Cognito)
Manage a Workforce (Amazon SageMaker Console)
Create a Work Team Using the SageMaker Console
Subscriptions
Add or Remove Workers
Add Workers to the Workforce
Add a Worker to a Work Team
Disable and Remove a Worker from the Workforce
Manage a Private Workforce (Amazon Cognito Console)
Create Work Teams (Amazon Cognito Console)
Subscriptions
Add and Remove Workers (Amazon Cognito Console)
Add a Worker to a Work Team
Disable and Remove a Worker From a Work Team
Create and Manage OIDC IdP Workforce
Create a Private Workforce (OIDC IdP)
Send Required and Optional Claims to Ground Truth and Amazon A2I
Create an OIDC IdP Workforce
Configure your OIDC IdP
Validate Your OIC IdP Workforce Authentication Response
Next Steps
Manage a Private Workforce (OIDC IdP)
Prerequisites
Add work teams
Add or remove IdP groups from work teams
Delete a work team
Manage Individual Workers
Update, Delete, and Describe Your Workforce
Manage Private Workforce Using the Amazon SageMaker API
Find Your Workforce Name
Restrict Worker Access to Tasks to Allowable IP Addresses
Update OIDC Identity Provider Workforce Configuration
Delete a Private Workforce
Track Worker Performance
Enable Tracking
Examine Logs
Use Log Metrics
Create and manage Amazon SNS topics for your work teams
Create the Amazon SNS topic
Manage worker subscriptions
Crowd HTML Elements Reference
SageMaker Crowd HTML Elements
crowd-alert
Attributes
dismissible
type
Element Hierarchy
See Also
crowd-badge
Attributes
for
icon
label
Element Hierarchy
See Also
crowd-button
Attributes
disabled
form-action
href
icon
icon-align
icon-url
loading
target
variant
Element Hierarchy
See Also
crowd-bounding-box
Attributes
header
initial-value
labels
name
src
Element Hierarchy
Regions
full-instructions
short-instructions
Output
boundingBoxes
inputImageProperties
See Also
crowd-card
Attributes
heading
image
Element Hierarchy
See Also
crowd-checkbox
Attributes
checked
disabled
name
required
value
Element Hierarchy
Output
See Also
crowd-classifier
Attributes
categories
header
name
Element Hierarchy
Regions
classification-target
full-instructions
short-instructions
Output
See Also
crowd-classifier-multi-select
Attributes
categories
header
name
exclusion-category
Element Hierarchy
Regions
classification-target
full-instructions
short-instructions
Output
See Also
crowd-entity-annotation
Attributes
header
initial-value
labels
name
text
Element Hierarchy
Regions
full-instructions
short-instructions
Output
entities
See Also
crowd-fab
Attributes
disabled
icon
label
title
Element Hierarchy
See Also
crowd-form
Element Hierarchy
Element Events
See Also
crowd-icon-button
Attributes
disabled
icon
Element Hierarchy
See Also
crowd-image-classifier
Attributes
categories
header
name
overlay
src
Element Hierarchy
Regions
full-instructions
short-instructions
worker-comment
header
link-text
placeholder
Output
See Also
crowd-image-classifier-multi-select
Attributes
categories
header
name
src
exclusion-category
Element Hierarchy
Regions
full-instructions
short-instructions
Output
See Also
crowd-input
Attributes
allowed-pattern
auto-focus
auto-validate
disabled
error-message
label
max-length
min-length
name
placeholder
required
type
value
Element Hierarchy
Output
See Also
crowd-instance-segmentation
Attributes
header
labels
name
src
initial-value
Element Hierarchy
Regions
full-instructions
short-instructions
Output
labeledImage
instances
inputImageProperties
See Also
crowd-instructions
Attributes
link-text
link-type
Element Hierarchy
Regions
detailed-instructions
negative-example
positive-example
short-summary
See Also
crowd-keypoint
Attributes
header
initial-value
labels
name
src
Element Hierarchy
Regions
full-instructions
short-instructions
Output
inputImageProperties
keypoints
See Also
crowd-line
Attributes
header
initial-value
labels
label-colors
name
src
Regions
full-instructions
short-instructions
Element Hierarchy
Output
inputImageProperties
lines
See Also
crowd-modal
Attributes
link-text
link-type
Element Hierarchy
See Also
crowd-polygon
Attributes
header
labels
name
src
initial-value
Element Hierarchy
Regions
full-instructions
short-instructions
Output
polygons
inputImageProperties
See Also
crowd-polyline
Attributes
header
initial-value
labels
label-colors
name
src
Regions
full-instructions
short-instructions
Element Hierarchy
Output
inputImageProperties
polylines
See Also
crowd-radio-button
Attributes
checked
disabled
name
value
Element Hierarchy
Output
See Also
crowd-radio-group
Attributes
Element Hierarchy
Output
See Also
crowd-semantic-segmentation
Attributes
header
initial-value
labels
name
src
Element Hierarchy
Regions
full-instructions
short-instructions
Output
labeledImage
labelMappings
initialValueModified
inputImageProperties
See Also
crowd-slider
Attributes
disabled
editable
max
min
name
pin
required
secondary-progress
step
value
Element Hierarchy
See Also
crowd-tab
Attributes
header
Element Hierarchy
See Also
crowd-tabs
Attributes
Element Hierarchy
See Also
crowd-text-area
Attributes
auto-focus
auto-validate
char-counter
disabled
error-message
label
max-length
max-rows
name
placeholder
rows
value
Element Hierarchy
Output
See Also
crowd-toast
Attributes
duration
text
Element Hierarchy
See Also
crowd-toggle-button
Attributes
checked
disabled
invalid
name
required
value
Element Hierarchy
Output
See Also
Augmented AI Crowd HTML Elements
crowd-textract-analyze-document
Attributes
header
src
initialValue
blockTypes
keys
no-key-edit
no-geometry-edit
Element Hierarchy
Regions
full-instructions
short-instructions
Example of a Worker Template Using the crowd Element
Output
crowd-rekognition-detect-moderation-labels
Attributes
header
src
categories
exclusion-category
Element Hierarchy
AWS Regions
full-instructions
short-instructions
Example Worker Template with the crowd Element
Output
Prepare and Analyze Datasets
Detect Pretraining Data Bias
Amazon SageMaker Clarify Terms for Bias and Fairness
Sample Notebooks
Measure Pretraining Bias
Class Imbalance (CI)
Difference in Proportions of Labels (DPL)
Kullback-Leibler Divergence (KL)
Jensen-Shannon Divergence (JS)
Lp-norm (LP)
Total Variation Distance (TVD)
Kolmogorov-Smirnov (KS)
Conditional Demographic Disparity (CDD)
Generate Reports for Bias in Pretraining Data in SageMaker Studio
Prepare ML Data with Amazon SageMaker Data Wrangler
Get Started with Data Wrangler
Prerequisites
Access Data Wrangler
Update Data Wrangler
Demo: Data Wrangler Titanic Dataset Walkthrough
Upload Dataset to S3 and Import
Data Flow
Prepare and Visualize
Data Exploration
Drop Unused Columns
Clean up Missing Values
Custom Pandas: Encode
Custom SQL: SELECT Columns
Export
Export to Data Wrangler Job Notebook
Training XGBoost Classifier
Shut down Data Wrangler
Import
Import data from Amazon S3
Import data from Athena
Import data from Amazon Redshift
Import data from Snowflake
Administrator Guide
Configure Snowflake with Data Wrangler
What information needs to be provided to the Data Scientist
Data Scientist Guide
Private Connectivity between Data Wrangler and Snowflake via AWS PrivateLink
Create a VPC
Set up Snowflake AWS PrivateLink Integration
Configure DNS for Snowflake Endpoints in your VPC
Configure Route 53 Resolver Inbound Endpoint for your VPC
SageMaker VPC Endpoints
Imported Data Storage
Amazon Redshift Import Storage
Amazon Athena Import Storage
Create and Use a Data Wrangler Flow
Instances
The Data Flow UI
Add a Step to Your Data Flow
Delete a step from your Data Flow
Transform Data
Transform UI
Join Datasets
Concatenate Datasets
Custom Transforms
Custom Formula
Encode Categorical
Ordinal Encode
One-Hot Encode
Featurize Text
Character Statistics
Vectorize
Featurize Date/Time
Format String
Handle Outliers
Robust standard deviation numeric outliers
Standard Deviation Numeric Outliers
Quantile Numeric Outliers
Min-Max Numeric Outliers
Replace Rare
Handle Missing Values
Fill Missing
Impute Missing
Add Indicator for Missing
Drop Missing
Manage Columns
Manage Rows
Manage Vectors
Process Numeric
Search and Edit
Parse Value as Type
Validate String
Analyze and Visualize
Histogram
Scatter Plot
Table Summary
Quick Model
Target Leakage
Bias Report
Create Custom Visualizations
Export
Export to a Data Wrangler Job
Export to SageMaker Pipelines
Use A Jupyter Notebook to Create a Pipeline
Export to Python Code
Export to the SageMaker Feature Store
Use A Jupyter Notebook to Add Features to a Feature Store
Export to Amazon S3
Shut Down Data Wrangler
Update Data Wrangler
Security and Permissions
Add a Bucket Policy To Restrict Access to Datasets Imported to Data Wrangler
Grant an IAM Role Permission to Use Data Wrangler
Snowflake and Data Wrangler
Data Encryption with KMS-CMK
Amazon S3 CMK setup for Data Wrangler imported data storage
Release Notes
Troubleshoot
Process Data
Use Amazon SageMaker Processing Sample Notebooks
Monitor Amazon SageMaker Processing Jobs with CloudWatch Logs and Metrics
Data Processing with Apache Spark
Running a Spark Processing Job
Data Processing with scikit-learn
Use Your Own Processing Code
Run Scripts with Your Own Processing Container
Build Your Own Processing Container (Advanced Scenario)
How Amazon SageMaker Processing Runs Your Processing Container Image
How Amazon SageMaker Processing Configures Input and Output For Your Processing Container
How Amazon SageMaker Processing Provides Logs and Metrics for Your Processing Container
How Amazon SageMaker Processing Configures Your Processing Container
Save and Access Metadata Information About Your Processing Job
Run Your Processing Container Using the SageMaker Python SDK
Create, Store, and Share Features with Amazon SageMaker Feature Store
How Feature Store Works
Create Feature Groups
Find, Discover, and Share Features
Real-Time Inference for Features Stored in the Online Store 
Offline Store for Model Training and Batch Inference
Feature Data Ingestion
Get started with Amazon SageMaker Feature Store
Feature Store Concepts
Create Feature Groups
Introduction to Feature Store
Step 1: Set Up
Step 2: Inspect your data
Step 3: Create feature groups
Step 4: Ingest data into a feature group
Step 5: Clean up
Step 6: Next steps
Step 7: Programmers note
Fraud Detection with Feature Store
Step 1: Set Up Feature Store
Step 2: Load Datasets and Partition Data into Feature Groups
Step 3: Set Up Feature Groups
Step 4: Set Up Record Identifier and Event Time Features
Step 5: Load Feature Definitions
Step 6: Create a Feature Group
Step 7: Work with Feature Groups
Describe a Feature Group
List Feature Groups
Put Records in a Feature Group
Get Records from a Feature Group
Generate Hive DDL Commands
Build a Training Dataset
Write and Execute an Athena Query
Delete a Feature Group
Adding required policies to your IAM role
Step 1: Access AWS Management Console
Step 2: Choose Roles
Step 3: Find your role
Step 4: Attach policy
Use Amazon SageMaker Feature Store with Amazon SageMaker Studio
Create a Feature Group in Studio
View Feature Group Details in Studio
Data Sources and Ingestion
Stream Ingestion
Data Wrangler with Feature Store
Query Feature Store with Athena and AWS Glue
Sample Athena Queries
Cross-Account Offline Store Access
Step 1: Set Up the Offline Store Access Role in Account A
Step 2: Set up an Offline Store S3 Bucket in Account B
Step 3: Set up an Offline Store KMS Encryption Key in Account A
Step 4: Create a Feature Group in Account A
Quotas, Naming Rules and Data Types
Limits and Quotas
Naming Rules
Data Types
Amazon SageMaker Feature Store Offline Store Data Format
Amazon SageMaker Feature Store Notebook Examples
Feature Store sample notebooks
Train Models
Choose an Algorithm
Choose an algorithm implementation
Use a built-in algorithm
Use script mode in a supported framework
Use a custom Docker image
Problem types for the basic machine learning paradigms
Supervised learning
Unsupervised learning
Reinforcement learning
Use Amazon SageMaker Built-in Algorithms
Supervised Learning
Unsupervised Learning
Textual Analysis
Image Processing
Common Information About Built-in Algorithms
Docker Registry Paths and Example Code
Docker Registry Paths and Example Code for US East (Ohio) (us-east-2)
BlazingText (algorithm)
Chainer (DLC)
Clarify (algorithm)
Data Wrangler (algorithm)
Debugger (algorithm)
DeepAR Forecasting (algorithm)
Factorization Machines (algorithm)
Hugging Face (algorithm)
IP Insights (algorithm)
Image classification (algorithm)
Inferentia MXNet (DLC)
Inferentia PyTorch (DLC)
K-Means (algorithm)
KNN (algorithm)
LDA (algorithm)
Linear Learner (algorithm)
MXNet (DLC)
MXNet Coach (DLC)
Model Monitor (algorithm)
NTM (algorithm)
Neo Image Classification (algorithm)
Neo MXNet (DLC)
Neo PyTorch (DLC)
Neo Tensorflow (DLC)
Neo XGBoost (algorithm)
Object Detection (algorithm)
Object2Vec (algorithm)
PCA (algorithm)
PyTorch (DLC)
Random Cut Forest (algorithm)
Ray PyTorch (DLC)
Scikit-learn (algorithm)
Semantic Segmentation (algorithm)
Seq2Seq (algorithm)
Spark (algorithm)
SparkML Serving (algorithm)
Tensorflow (DLC)
Tensorflow Coach (DLC)
Tensorflow Inferentia (DLC)
Tensorflow Ray (DLC)
VW (algorithm)
XGBoost (algorithm)
Docker Registry Paths and Example Code for US East (N. Virginia) (us-east-1)
BlazingText (algorithm)
Chainer (DLC)
Clarify (algorithm)
Data Wrangler (algorithm)
Debugger (algorithm)
DeepAR Forecasting (algorithm)
Factorization Machines (algorithm)
Hugging Face (algorithm)
IP Insights (algorithm)
Image classification (algorithm)
Inferentia MXNet (DLC)
Inferentia PyTorch (DLC)
K-Means (algorithm)
KNN (algorithm)
LDA (algorithm)
Linear Learner (algorithm)
MXNet (DLC)
MXNet Coach (DLC)
Model Monitor (algorithm)
NTM (algorithm)
Neo Image Classification (algorithm)
Neo MXNet (DLC)
Neo PyTorch (DLC)
Neo Tensorflow (DLC)
Neo XGBoost (algorithm)
Object Detection (algorithm)
Object2Vec (algorithm)
PCA (algorithm)
PyTorch (DLC)
Random Cut Forest (algorithm)
Ray PyTorch (DLC)
Scikit-learn (algorithm)
Semantic Segmentation (algorithm)
Seq2Seq (algorithm)
Spark (algorithm)
SparkML Serving (algorithm)
Tensorflow (DLC)
Tensorflow Coach (DLC)
Tensorflow Inferentia (DLC)
Tensorflow Ray (DLC)
VW (algorithm)
XGBoost (algorithm)
Docker Registry Paths and Example Code for US West (N. California) (us-west-1)
BlazingText (algorithm)
Chainer (DLC)
Clarify (algorithm)
Data Wrangler (algorithm)
Debugger (algorithm)
DeepAR Forecasting (algorithm)
Factorization Machines (algorithm)
Hugging Face (algorithm)
IP Insights (algorithm)
Image classification (algorithm)
Inferentia MXNet (DLC)
Inferentia PyTorch (DLC)
K-Means (algorithm)
KNN (algorithm)
LDA (algorithm)
Linear Learner (algorithm)
MXNet (DLC)
MXNet Coach (DLC)
Model Monitor (algorithm)
NTM (algorithm)
Neo Image Classification (algorithm)
Neo MXNet (DLC)
Neo PyTorch (DLC)
Neo Tensorflow (DLC)
Neo XGBoost (algorithm)
Object Detection (algorithm)
Object2Vec (algorithm)
PCA (algorithm)
PyTorch (DLC)
Random Cut Forest (algorithm)
Ray PyTorch (DLC)
Scikit-learn (algorithm)
Semantic Segmentation (algorithm)
Seq2Seq (algorithm)
Spark (algorithm)
SparkML Serving (algorithm)
Tensorflow (DLC)
Tensorflow Coach (DLC)
Tensorflow Inferentia (DLC)
Tensorflow Ray (DLC)
VW (algorithm)
XGBoost (algorithm)
Docker Registry Paths and Example Code for US West (Oregon) (us-west-2)
BlazingText (algorithm)
Chainer (DLC)
Clarify (algorithm)
Data Wrangler (algorithm)
Debugger (algorithm)
DeepAR Forecasting (algorithm)
Factorization Machines (algorithm)
Hugging Face (algorithm)
IP Insights (algorithm)
Image classification (algorithm)
Inferentia MXNet (DLC)
Inferentia PyTorch (DLC)
K-Means (algorithm)
KNN (algorithm)
LDA (algorithm)
Linear Learner (algorithm)
MXNet (DLC)
MXNet Coach (DLC)
Model Monitor (algorithm)
NTM (algorithm)
Neo Image Classification (algorithm)
Neo MXNet (DLC)
Neo PyTorch (DLC)
Neo Tensorflow (DLC)
Neo XGBoost (algorithm)
Object Detection (algorithm)
Object2Vec (algorithm)
PCA (algorithm)
PyTorch (DLC)
Random Cut Forest (algorithm)
Ray PyTorch (DLC)
Scikit-learn (algorithm)
Semantic Segmentation (algorithm)
Seq2Seq (algorithm)
Spark (algorithm)
SparkML Serving (algorithm)
Tensorflow (DLC)
Tensorflow Coach (DLC)
Tensorflow Inferentia (DLC)
Tensorflow Ray (DLC)
VW (algorithm)
XGBoost (algorithm)
Docker Registry Paths and Example Code for Africa (Cape Town) (af-south-1)
BlazingText (algorithm)
Chainer (DLC)
Clarify (algorithm)
Data Wrangler (algorithm)
Debugger (algorithm)
DeepAR Forecasting (algorithm)
Factorization Machines (algorithm)
Hugging Face (algorithm)
IP Insights (algorithm)
Image classification (algorithm)
Inferentia MXNet (DLC)
Inferentia PyTorch (DLC)
K-Means (algorithm)
KNN (algorithm)
Linear Learner (algorithm)
MXNet (DLC)
MXNet Coach (DLC)
Model Monitor (algorithm)
NTM (algorithm)
Neo Image Classification (algorithm)
Neo MXNet (DLC)
Neo PyTorch (DLC)
Neo Tensorflow (DLC)
Neo XGBoost (algorithm)
Object Detection (algorithm)
Object2Vec (algorithm)
PCA (algorithm)
PyTorch (DLC)
Random Cut Forest (algorithm)
Scikit-learn (algorithm)
Semantic Segmentation (algorithm)
Seq2Seq (algorithm)
Spark (algorithm)
SparkML Serving (algorithm)
Tensorflow (DLC)
Tensorflow Coach (DLC)
Tensorflow Inferentia (DLC)
Tensorflow Ray (DLC)
XGBoost (algorithm)
Docker Registry Paths and Example Code for Asia Pacific (Hong Kong) (ap-east-1)
BlazingText (algorithm)
Chainer (DLC)
Clarify (algorithm)
Data Wrangler (algorithm)
Debugger (algorithm)
DeepAR Forecasting (algorithm)
Factorization Machines (algorithm)
Hugging Face (algorithm)
IP Insights (algorithm)
Image classification (algorithm)
Inferentia MXNet (DLC)
Inferentia PyTorch (DLC)
K-Means (algorithm)
KNN (algorithm)
Linear Learner (algorithm)
MXNet (DLC)
MXNet Coach (DLC)
Model Monitor (algorithm)
NTM (algorithm)
Neo Image Classification (algorithm)
Neo MXNet (DLC)
Neo PyTorch (DLC)
Neo Tensorflow (DLC)
Neo XGBoost (algorithm)
Object Detection (algorithm)
Object2Vec (algorithm)
PCA (algorithm)
PyTorch (DLC)
Random Cut Forest (algorithm)
Scikit-learn (algorithm)
Semantic Segmentation (algorithm)
Seq2Seq (algorithm)
Spark (algorithm)
SparkML Serving (algorithm)
Tensorflow (DLC)
Tensorflow Coach (DLC)
Tensorflow Inferentia (DLC)
Tensorflow Ray (DLC)
XGBoost (algorithm)
Docker Registry Paths and Example Code for Asia Pacific (Mumbai) (ap-south-1)
BlazingText (algorithm)
Chainer (DLC)
Clarify (algorithm)
Data Wrangler (algorithm)
Debugger (algorithm)
DeepAR Forecasting (algorithm)
Factorization Machines (algorithm)
Hugging Face (algorithm)
IP Insights (algorithm)
Image classification (algorithm)
Inferentia MXNet (DLC)
Inferentia PyTorch (DLC)
K-Means (algorithm)
KNN (algorithm)
LDA (algorithm)
Linear Learner (algorithm)
MXNet (DLC)
MXNet Coach (DLC)
Model Monitor (algorithm)
NTM (algorithm)
Neo Image Classification (algorithm)
Neo MXNet (DLC)
Neo PyTorch (DLC)
Neo Tensorflow (DLC)
Neo XGBoost (algorithm)
Object Detection (algorithm)
Object2Vec (algorithm)
PCA (algorithm)
PyTorch (DLC)
Random Cut Forest (algorithm)
Ray PyTorch (DLC)
Scikit-learn (algorithm)
Semantic Segmentation (algorithm)
Seq2Seq (algorithm)
Spark (algorithm)
SparkML Serving (algorithm)
Tensorflow (DLC)
Tensorflow Coach (DLC)
Tensorflow Inferentia (DLC)
Tensorflow Ray (DLC)
VW (algorithm)
XGBoost (algorithm)
Docker Registry Paths and Example Code for Asia Pacific (Seoul) (ap-northeast-2)
BlazingText (algorithm)
Chainer (DLC)
Clarify (algorithm)
Data Wrangler (algorithm)
Debugger (algorithm)
DeepAR Forecasting (algorithm)
Factorization Machines (algorithm)
Hugging Face (algorithm)
IP Insights (algorithm)
Image classification (algorithm)
Inferentia MXNet (DLC)
Inferentia PyTorch (DLC)
K-Means (algorithm)
KNN (algorithm)
LDA (algorithm)
Linear Learner (algorithm)
MXNet (DLC)
MXNet Coach (DLC)
Model Monitor (algorithm)
NTM (algorithm)
Neo Image Classification (algorithm)
Neo MXNet (DLC)
Neo PyTorch (DLC)
Neo Tensorflow (DLC)
Neo XGBoost (algorithm)
Object Detection (algorithm)
Object2Vec (algorithm)
PCA (algorithm)
PyTorch (DLC)
Random Cut Forest (algorithm)
Ray PyTorch (DLC)
Scikit-learn (algorithm)
Semantic Segmentation (algorithm)
Seq2Seq (algorithm)
Spark (algorithm)
SparkML Serving (algorithm)
Tensorflow (DLC)
Tensorflow Coach (DLC)
Tensorflow Inferentia (DLC)
Tensorflow Ray (DLC)
VW (algorithm)
XGBoost (algorithm)
Docker Registry Paths and Example Code for Asia Pacific (Singapore) (ap-southeast-1)
BlazingText (algorithm)
Chainer (DLC)
Clarify (algorithm)
Data Wrangler (algorithm)
Debugger (algorithm)
DeepAR Forecasting (algorithm)
Factorization Machines (algorithm)
Hugging Face (algorithm)
IP Insights (algorithm)
Image classification (algorithm)
Inferentia MXNet (DLC)
Inferentia PyTorch (DLC)
K-Means (algorithm)
KNN (algorithm)
LDA (algorithm)
Linear Learner (algorithm)
MXNet (DLC)
MXNet Coach (DLC)
Model Monitor (algorithm)
NTM (algorithm)
Neo Image Classification (algorithm)
Neo MXNet (DLC)
Neo PyTorch (DLC)
Neo Tensorflow (DLC)
Neo XGBoost (algorithm)
Object Detection (algorithm)
Object2Vec (algorithm)
PCA (algorithm)
PyTorch (DLC)
Random Cut Forest (algorithm)
Ray PyTorch (DLC)
Scikit-learn (algorithm)
Semantic Segmentation (algorithm)
Seq2Seq (algorithm)
Spark (algorithm)
SparkML Serving (algorithm)
Tensorflow (DLC)
Tensorflow Coach (DLC)
Tensorflow Inferentia (DLC)
Tensorflow Ray (DLC)
VW (algorithm)
XGBoost (algorithm)
Docker Registry Paths and Example Code for Asia Pacific (Sydney) (ap-southeast-2)
BlazingText (algorithm)
Chainer (DLC)
Clarify (algorithm)
Data Wrangler (algorithm)
Debugger (algorithm)
DeepAR Forecasting (algorithm)
Factorization Machines (algorithm)
Hugging Face (algorithm)
IP Insights (algorithm)
Image classification (algorithm)
Inferentia MXNet (DLC)
Inferentia PyTorch (DLC)
K-Means (algorithm)
KNN (algorithm)
LDA (algorithm)
Linear Learner (algorithm)
MXNet (DLC)
MXNet Coach (DLC)
Model Monitor (algorithm)
NTM (algorithm)
Neo Image Classification (algorithm)
Neo MXNet (DLC)
Neo PyTorch (DLC)
Neo Tensorflow (DLC)
Neo XGBoost (algorithm)
Object Detection (algorithm)
Object2Vec (algorithm)
PCA (algorithm)
PyTorch (DLC)
Random Cut Forest (algorithm)
Ray PyTorch (DLC)
Scikit-learn (algorithm)
Semantic Segmentation (algorithm)
Seq2Seq (algorithm)
Spark (algorithm)
SparkML Serving (algorithm)
Tensorflow (DLC)
Tensorflow Coach (DLC)
Tensorflow Inferentia (DLC)
Tensorflow Ray (DLC)
VW (algorithm)
XGBoost (algorithm)
Docker Registry Paths and Example Code for Asia Pacific (Tokyo) (ap-northeast-1)
BlazingText (algorithm)
Chainer (DLC)
Clarify (algorithm)
Data Wrangler (algorithm)
Debugger (algorithm)
DeepAR Forecasting (algorithm)
Factorization Machines (algorithm)
Hugging Face (algorithm)
IP Insights (algorithm)
Image classification (algorithm)
Inferentia MXNet (DLC)
Inferentia PyTorch (DLC)
K-Means (algorithm)
KNN (algorithm)
LDA (algorithm)
Linear Learner (algorithm)
MXNet (DLC)
MXNet Coach (DLC)
Model Monitor (algorithm)
NTM (algorithm)
Neo Image Classification (algorithm)
Neo MXNet (DLC)
Neo PyTorch (DLC)
Neo Tensorflow (DLC)
Neo XGBoost (algorithm)
Object Detection (algorithm)
Object2Vec (algorithm)
PCA (algorithm)
PyTorch (DLC)
Random Cut Forest (algorithm)
Ray PyTorch (DLC)
Scikit-learn (algorithm)
Semantic Segmentation (algorithm)
Seq2Seq (algorithm)
Spark (algorithm)
SparkML Serving (algorithm)
Tensorflow (DLC)
Tensorflow Coach (DLC)
Tensorflow Inferentia (DLC)
Tensorflow Ray (DLC)
VW (algorithm)
XGBoost (algorithm)
Docker Registry Paths and Example Code for Canada (Central) (ca-central-1)
BlazingText (algorithm)
Chainer (DLC)
Clarify (algorithm)
Data Wrangler (algorithm)
Debugger (algorithm)
DeepAR Forecasting (algorithm)
Factorization Machines (algorithm)
Hugging Face (algorithm)
IP Insights (algorithm)
Image classification (algorithm)
Inferentia MXNet (DLC)
Inferentia PyTorch (DLC)
K-Means (algorithm)
KNN (algorithm)
LDA (algorithm)
Linear Learner (algorithm)
MXNet (DLC)
MXNet Coach (DLC)
Model Monitor (algorithm)
NTM (algorithm)
Neo Image Classification (algorithm)
Neo MXNet (DLC)
Neo PyTorch (DLC)
Neo Tensorflow (DLC)
Neo XGBoost (algorithm)
Object Detection (algorithm)
Object2Vec (algorithm)
PCA (algorithm)
PyTorch (DLC)
Random Cut Forest (algorithm)
Ray PyTorch (DLC)
Scikit-learn (algorithm)
Semantic Segmentation (algorithm)
Seq2Seq (algorithm)
Spark (algorithm)
SparkML Serving (algorithm)
Tensorflow (DLC)
Tensorflow Coach (DLC)
Tensorflow Inferentia (DLC)
Tensorflow Ray (DLC)
VW (algorithm)
XGBoost (algorithm)
Docker Registry Paths and Example Code for China (Beijing) (cn-north-1)
BlazingText (algorithm)
Chainer (DLC)
Clarify (algorithm)
Data Wrangler (algorithm)
Debugger (algorithm)
DeepAR Forecasting (algorithm)
Factorization Machines (algorithm)
Hugging Face (algorithm)
IP Insights (algorithm)
Image classification (algorithm)
Inferentia MXNet (DLC)
Inferentia PyTorch (DLC)
K-Means (algorithm)
KNN (algorithm)
Linear Learner (algorithm)
MXNet (DLC)
MXNet Coach (DLC)
Model Monitor (algorithm)
NTM (algorithm)
Neo Image Classification (algorithm)
Neo MXNet (DLC)
Neo PyTorch (DLC)
Neo Tensorflow (DLC)
Neo XGBoost (algorithm)
Object Detection (algorithm)
Object2Vec (algorithm)
PCA (algorithm)
PyTorch (DLC)
Random Cut Forest (algorithm)
Scikit-learn (algorithm)
Semantic Segmentation (algorithm)
Seq2Seq (algorithm)
Spark (algorithm)
SparkML Serving (algorithm)
Tensorflow (DLC)
Tensorflow Coach (DLC)
Tensorflow Inferentia (DLC)
Tensorflow Ray (DLC)
XGBoost (algorithm)
Docker Registry Paths and Example Code for China (Ningxia) (cn-northwest-1)
BlazingText (algorithm)
Chainer (DLC)
Clarify (algorithm)
Data Wrangler (algorithm)
Debugger (algorithm)
DeepAR Forecasting (algorithm)
Factorization Machines (algorithm)
Hugging Face (algorithm)
IP Insights (algorithm)
Image classification (algorithm)
Inferentia MXNet (DLC)
Inferentia PyTorch (DLC)
K-Means (algorithm)
KNN (algorithm)
Linear Learner (algorithm)
MXNet (DLC)
MXNet Coach (DLC)
Model Monitor (algorithm)
NTM (algorithm)
Neo Image Classification (algorithm)
Neo MXNet (DLC)
Neo PyTorch (DLC)
Neo Tensorflow (DLC)
Neo XGBoost (algorithm)
Object Detection (algorithm)
Object2Vec (algorithm)
PCA (algorithm)
PyTorch (DLC)
Random Cut Forest (algorithm)
Scikit-learn (algorithm)
Semantic Segmentation (algorithm)
Seq2Seq (algorithm)
Spark (algorithm)
SparkML Serving (algorithm)
Tensorflow (DLC)
Tensorflow Coach (DLC)
Tensorflow Inferentia (DLC)
Tensorflow Ray (DLC)
XGBoost (algorithm)
Docker Registry Paths and Example Code for Europe (Frankfurt) (eu-central-1)
BlazingText (algorithm)
Chainer (DLC)
Clarify (algorithm)
Data Wrangler (algorithm)
Debugger (algorithm)
DeepAR Forecasting (algorithm)
Factorization Machines (algorithm)
Hugging Face (algorithm)
IP Insights (algorithm)
Image classification (algorithm)
Inferentia MXNet (DLC)
Inferentia PyTorch (DLC)
K-Means (algorithm)
KNN (algorithm)
LDA (algorithm)
Linear Learner (algorithm)
MXNet (DLC)
MXNet Coach (DLC)
Model Monitor (algorithm)
NTM (algorithm)
Neo Image Classification (algorithm)
Neo MXNet (DLC)
Neo PyTorch (DLC)
Neo Tensorflow (DLC)
Neo XGBoost (algorithm)
Object Detection (algorithm)
Object2Vec (algorithm)
PCA (algorithm)
PyTorch (DLC)
Random Cut Forest (algorithm)
Ray PyTorch (DLC)
Scikit-learn (algorithm)
Semantic Segmentation (algorithm)
Seq2Seq (algorithm)
Spark (algorithm)
SparkML Serving (algorithm)
Tensorflow (DLC)
Tensorflow Coach (DLC)
Tensorflow Inferentia (DLC)
Tensorflow Ray (DLC)
VW (algorithm)
XGBoost (algorithm)
Docker Registry Paths and Example Code for Europe (Ireland) (eu-west-1)
BlazingText (algorithm)
Chainer (DLC)
Clarify (algorithm)
Data Wrangler (algorithm)
Debugger (algorithm)
DeepAR Forecasting (algorithm)
Factorization Machines (algorithm)
Hugging Face (algorithm)
IP Insights (algorithm)
Image classification (algorithm)
Inferentia MXNet (DLC)
Inferentia PyTorch (DLC)
K-Means (algorithm)
KNN (algorithm)
LDA (algorithm)
Linear Learner (algorithm)
MXNet (DLC)
MXNet Coach (DLC)
Model Monitor (algorithm)
NTM (algorithm)
Neo Image Classification (algorithm)
Neo MXNet (DLC)
Neo PyTorch (DLC)
Neo Tensorflow (DLC)
Neo XGBoost (algorithm)
Object Detection (algorithm)
Object2Vec (algorithm)
PCA (algorithm)
PyTorch (DLC)
Random Cut Forest (algorithm)
Ray PyTorch (DLC)
Scikit-learn (algorithm)
Semantic Segmentation (algorithm)
Seq2Seq (algorithm)
Spark (algorithm)
SparkML Serving (algorithm)
Tensorflow (DLC)
Tensorflow Coach (DLC)
Tensorflow Inferentia (DLC)
Tensorflow Ray (DLC)
VW (algorithm)
XGBoost (algorithm)
Docker Registry Paths and Example Code for Europe (London) (eu-west-2)
BlazingText (algorithm)
Chainer (DLC)
Clarify (algorithm)
Data Wrangler (algorithm)
Debugger (algorithm)
DeepAR Forecasting (algorithm)
Factorization Machines (algorithm)
Hugging Face (algorithm)
IP Insights (algorithm)
Image classification (algorithm)
Inferentia MXNet (DLC)
Inferentia PyTorch (DLC)
K-Means (algorithm)
KNN (algorithm)
LDA (algorithm)
Linear Learner (algorithm)
MXNet (DLC)
MXNet Coach (DLC)
Model Monitor (algorithm)
NTM (algorithm)
Neo Image Classification (algorithm)
Neo MXNet (DLC)
Neo PyTorch (DLC)
Neo Tensorflow (DLC)
Neo XGBoost (algorithm)
Object Detection (algorithm)
Object2Vec (algorithm)
PCA (algorithm)
PyTorch (DLC)
Random Cut Forest (algorithm)
Ray PyTorch (DLC)
Scikit-learn (algorithm)
Semantic Segmentation (algorithm)
Seq2Seq (algorithm)
Spark (algorithm)
SparkML Serving (algorithm)
Tensorflow (DLC)
Tensorflow Coach (DLC)
Tensorflow Inferentia (DLC)
Tensorflow Ray (DLC)
VW (algorithm)
XGBoost (algorithm)
Docker Registry Paths and Example Code for Europe (Paris) (eu-west-3)
BlazingText (algorithm)
Chainer (DLC)
Clarify (algorithm)
Data Wrangler (algorithm)
Debugger (algorithm)
DeepAR Forecasting (algorithm)
Factorization Machines (algorithm)
Hugging Face (algorithm)
IP Insights (algorithm)
Image classification (algorithm)
Inferentia MXNet (DLC)
Inferentia PyTorch (DLC)
K-Means (algorithm)
KNN (algorithm)
Linear Learner (algorithm)
MXNet (DLC)
MXNet Coach (DLC)
Model Monitor (algorithm)
NTM (algorithm)
Neo Image Classification (algorithm)
Neo MXNet (DLC)
Neo PyTorch (DLC)
Neo Tensorflow (DLC)
Neo XGBoost (algorithm)
Object Detection (algorithm)
Object2Vec (algorithm)
PCA (algorithm)
PyTorch (DLC)
Random Cut Forest (algorithm)
Scikit-learn (algorithm)
Semantic Segmentation (algorithm)
Seq2Seq (algorithm)
Spark (algorithm)
SparkML Serving (algorithm)
Tensorflow (DLC)
Tensorflow Coach (DLC)
Tensorflow Inferentia (DLC)
Tensorflow Ray (DLC)
XGBoost (algorithm)
Docker Registry Paths and Example Code for Europe (Stockholm) (eu-north-1)
BlazingText (algorithm)
Chainer (DLC)
Clarify (algorithm)
Data Wrangler (algorithm)
Debugger (algorithm)
DeepAR Forecasting (algorithm)
Factorization Machines (algorithm)
Hugging Face (algorithm)
IP Insights (algorithm)
Image classification (algorithm)
Inferentia MXNet (DLC)
Inferentia PyTorch (DLC)
K-Means (algorithm)
KNN (algorithm)
Linear Learner (algorithm)
MXNet (DLC)
MXNet Coach (DLC)
Model Monitor (algorithm)
NTM (algorithm)
Neo Image Classification (algorithm)
Neo MXNet (DLC)
Neo PyTorch (DLC)
Neo Tensorflow (DLC)
Neo XGBoost (algorithm)
Object Detection (algorithm)
Object2Vec (algorithm)
PCA (algorithm)
PyTorch (DLC)
Random Cut Forest (algorithm)
Scikit-learn (algorithm)
Semantic Segmentation (algorithm)
Seq2Seq (algorithm)
Spark (algorithm)
SparkML Serving (algorithm)
Tensorflow (DLC)
Tensorflow Coach (DLC)
Tensorflow Inferentia (DLC)
Tensorflow Ray (DLC)
XGBoost (algorithm)
Docker Registry Paths and Example Code for Europe (Milan) (eu-south-1)
BlazingText (algorithm)
Chainer (DLC)
Clarify (algorithm)
Data Wrangler (algorithm)
Debugger (algorithm)
DeepAR Forecasting (algorithm)
Factorization Machines (algorithm)
Hugging Face (algorithm)
IP Insights (algorithm)
Image classification (algorithm)
Inferentia MXNet (DLC)
Inferentia PyTorch (DLC)
K-Means (algorithm)
KNN (algorithm)
Linear Learner (algorithm)
MXNet (DLC)
MXNet Coach (DLC)
Model Monitor (algorithm)
NTM (algorithm)
Neo Image Classification (algorithm)
Neo MXNet (DLC)
Neo PyTorch (DLC)
Neo Tensorflow (DLC)
Neo XGBoost (algorithm)
Object Detection (algorithm)
Object2Vec (algorithm)
PCA (algorithm)
PyTorch (DLC)
Random Cut Forest (algorithm)
Scikit-learn (algorithm)
Semantic Segmentation (algorithm)
Seq2Seq (algorithm)
Spark (algorithm)
SparkML Serving (algorithm)
Tensorflow (DLC)
Tensorflow Coach (DLC)
Tensorflow Inferentia (DLC)
Tensorflow Ray (DLC)
XGBoost (algorithm)
Docker Registry Paths and Example Code for Middle East (Bahrain) (me-south-1)
BlazingText (algorithm)
Chainer (DLC)
Clarify (algorithm)
Data Wrangler (algorithm)
Debugger (algorithm)
DeepAR Forecasting (algorithm)
Factorization Machines (algorithm)
Hugging Face (algorithm)
IP Insights (algorithm)
Image classification (algorithm)
Inferentia MXNet (DLC)
Inferentia PyTorch (DLC)
K-Means (algorithm)
KNN (algorithm)
Linear Learner (algorithm)
MXNet (DLC)
MXNet Coach (DLC)
Model Monitor (algorithm)
NTM (algorithm)
Neo Image Classification (algorithm)
Neo MXNet (DLC)
Neo PyTorch (DLC)
Neo Tensorflow (DLC)
Neo XGBoost (algorithm)
Object Detection (algorithm)
Object2Vec (algorithm)
PCA (algorithm)
PyTorch (DLC)
Random Cut Forest (algorithm)
Scikit-learn (algorithm)
Semantic Segmentation (algorithm)
Seq2Seq (algorithm)
Spark (algorithm)
SparkML Serving (algorithm)
Tensorflow (DLC)
Tensorflow Coach (DLC)
Tensorflow Inferentia (DLC)
Tensorflow Ray (DLC)
XGBoost (algorithm)
Docker Registry Paths and Example Code for South America (São Paulo) (sa-east-1)
BlazingText (algorithm)
Chainer (DLC)
Clarify (algorithm)
Data Wrangler (algorithm)
Debugger (algorithm)
DeepAR Forecasting (algorithm)
Factorization Machines (algorithm)
Hugging Face (algorithm)
IP Insights (algorithm)
Image classification (algorithm)
Inferentia MXNet (DLC)
Inferentia PyTorch (DLC)
K-Means (algorithm)
KNN (algorithm)
Linear Learner (algorithm)
MXNet (DLC)
MXNet Coach (DLC)
Model Monitor (algorithm)
NTM (algorithm)
Neo Image Classification (algorithm)
Neo MXNet (DLC)
Neo PyTorch (DLC)
Neo Tensorflow (DLC)
Neo XGBoost (algorithm)
Object Detection (algorithm)
Object2Vec (algorithm)
PCA (algorithm)
PyTorch (DLC)
Random Cut Forest (algorithm)
Scikit-learn (algorithm)
Semantic Segmentation (algorithm)
Seq2Seq (algorithm)
Spark (algorithm)
SparkML Serving (algorithm)
Tensorflow (DLC)
Tensorflow Coach (DLC)
Tensorflow Inferentia (DLC)
Tensorflow Ray (DLC)
XGBoost (algorithm)
Docker Registry Paths and Example Code for AWS GovCloud (US-West) (us-gov-west-1)
BlazingText (algorithm)
Chainer (DLC)
Debugger (algorithm)
DeepAR Forecasting (algorithm)
Factorization Machines (algorithm)
Hugging Face (algorithm)
IP Insights (algorithm)
Image classification (algorithm)
Inferentia MXNet (DLC)
Inferentia PyTorch (DLC)
K-Means (algorithm)
KNN (algorithm)
LDA (algorithm)
Linear Learner (algorithm)
MXNet (DLC)
MXNet Coach (DLC)
NTM (algorithm)
Neo Image Classification (algorithm)
Neo MXNet (DLC)
Neo PyTorch (DLC)
Neo Tensorflow (DLC)
Neo XGBoost (algorithm)
Object Detection (algorithm)
Object2Vec (algorithm)
PCA (algorithm)
PyTorch (DLC)
Random Cut Forest (algorithm)
Scikit-learn (algorithm)
Semantic Segmentation (algorithm)
Seq2Seq (algorithm)
Spark (algorithm)
SparkML Serving (algorithm)
Tensorflow (DLC)
Tensorflow Coach (DLC)
Tensorflow Inferentia (DLC)
Tensorflow Ray (DLC)
XGBoost (algorithm)
Common Data Formats for Built-in Algorithms
Common Data Formats for Training
Content Types Supported by Built-In Algorithms
Using Pipe Mode
Using CSV Format
Using RecordIO Format
Trained Model Deserialization
Common Data Formats for Inference
Convert Data for Inference Request Serialization
Convert Data for Inference Response Deserialization
Common Request Formats for All Algorithms
JSON Request Format
JSONLINES Request Format
CSV Request Format
RECORDIO Request Format
Use Batch Transform with Built-in Algorithms
Instance Types for Built-in Algorithms
Logs for Built-in Algorithms
Common Errors
BlazingText algorithm
Input/Output Interface for the BlazingText Algorithm
Training and Validation Data Format
Training and Validation Data Format for the Word2Vec Algorithm
Training and Validation Data Format for the Text Classification Algorithm
Train with File Mode
Train with Augmented Manifest Text Format
Model Artifacts and Inference
Model Artifacts for the Word2Vec Algorithm
Sample JSON Request
Model Artifacts for the Text Classification Algorithm
Sample JSON Request
EC2 Instance Recommendation for the BlazingText Algorithm
BlazingText Sample Notebooks
BlazingText Hyperparameters
Word2Vec Hyperparameters
Text Classification Hyperparameters
Tune a BlazingText Model
Metrics Computed by the BlazingText Algorithm
Tunable BlazingText Hyperparameters
Tunable Hyperparameters for the Word2Vec Algorithm
Tunable Hyperparameters for the Text Classification Algorithm
DeepAR Forecasting Algorithm
Input/Output Interface for the DeepAR Algorithm
Best Practices for Using the DeepAR Algorithm
EC2 Instance Recommendations for the DeepAR Algorithm
DeepAR Sample Notebooks
How the DeepAR Algorithm Works
How Feature Time Series Work in the DeepAR Algorithm
DeepAR Hyperparameters
Tune a DeepAR Model
Metrics Computed by the DeepAR Algorithm
Tunable Hyperparameters for the DeepAR Algorithm
DeepAR Inference Formats
DeepAR JSON Request Formats
DeepAR JSON Response Formats
Batch Transform with the DeepAR Algorithm
Factorization Machines Algorithm
Input/Output Interface for the Factorization Machines Algorithm
EC2 Instance Recommendation for the Factorization Machines Algorithm
Factorization Machines Sample Notebooks
How Factorization Machines Work
Factorization Machines Hyperparameters
Tune a Factorization Machines Model
Metrics Computed by the Factorization Machines Algorithm
Tunable Factorization Machines Hyperparameters
Factorization Machines Response Formats
JSON Response Format
JSONLINES Response Format
RECORDIO Response Format
Image Classification Algorithm
Input/Output Interface for the Image Classification Algorithm
Train with RecordIO Format
Train with Image Format
Train with Augmented Manifest Image Format
Incremental Training
Inference with the Image Classification Algorithm
EC2 Instance Recommendation for the Image Classification Algorithm
Image Classification Sample Notebooks
How Image Classification Works
Image Classification Hyperparameters
Tune an Image Classification Model
Metrics Computed by the Image Classification Algorithm
Tunable Image Classification Hyperparameters
IP Insights
Input/Output Interface for the IP Insights Algorithm
EC2 Instance Recommendation for the IP Insights Algorithm
GPU Instances for the IP Insights Algorithm
CPU Instances for the IP Insights Algorithm
IP Insights Sample Notebooks
How IP Insights Works
IP Insights Hyperparameters
Tune an IP Insights Model
Metrics Computed by the IP Insights Algorithm
Tunable IP Insights Hyperparameters
IP Insights Data Formats
IP Insights Training Data Formats
IP Insights Training Data Input Formats
INPUT: CSV
IP Insights Inference Data Formats
IP Insights Input Request Formats
INPUT: CSV Format
INPUT: JSON Format
INPUT: JSONLINES Format
IP Insights Output Response Formats
OUTPUT: JSON Response Format
OUTPUT: JSONLINES Response Format
K-Means Algorithm
Input/Output Interface for the K-Means Algorithm
EC2 Instance Recommendation for the K-Means Algorithm
K-Means Sample Notebooks
How K-Means Clustering Works
Step 1: Determine the Initial Cluster Centers
Step 2: Iterate over the Training Dataset and Calculate Cluster Centers
Step 3: Reduce the Clusters from K to k
K-Means Hyperparameters
Tune a K-Means Model
Metrics Computed by the K-Means Algorithm
Tunable K-Means Hyperparameters
K-Means Response Formats
JSON Response Format
JSONLINES Response Format
RECORDIO Response Format
CSV Response Format
K-Nearest Neighbors (k-NN) Algorithm
Input/Output Interface for the k-NN Algorithm
k-NN Sample Notebooks
How the k-NN Algorithm Works
Step 1: Sample
Step 2: Perform Dimension Reduction
Step 3: Build an Index
Serialize the Model
EC2 Instance Recommendation for the k-NN Algorithm
Instance Recommendation for Training with the k-NN Algorithm
Instance Recommendation for Inference with the k-NN Algorithm
k-NN Hyperparameters
Tune a k-NN Model
Metrics Computed by the k-NN Algorithm
Tunable k-NN Hyperparameters
Data Formats for k-NN Training Input
CSV Data Format
RECORDIO Data Format
k-NN Request and Response Formats
INPUT: CSV Request Format
INPUT: JSON Request Format
INPUT: JSONLINES Request Format
INPUT: RECORDIO Request Format
OUTPUT: JSON Response Format
OUTPUT: JSONLINES Response Format
OUTPUT: VERBOSE JSON Response Format
OUTPUT: RECORDIO-PROTOBUF Response Format
OUTPUT: VERBOSE RECORDIO-PROTOBUF Response Format
SAMPLE OUTPUT for the k-NN Algorithm
Latent Dirichlet Allocation (LDA) Algorithm
Choosing between Latent Dirichlet Allocation (LDA) and Neural Topic Model (NTM)
Input/Output Interface for the LDA Algorithm
EC2 Instance Recommendation for the LDA Algorithm
LDA Sample Notebooks
How LDA Works
LDA Hyperparameters
Tune an LDA Model
Metrics Computed by the LDA Algorithm
Tunable LDA Hyperparameters
Linear Learner Algorithm
Input/Output interface for the linear learner algorithm
EC2 instance recommendation for the linear learner algorithm
Linear learner sample notebooks
How linear learner works
Step 1: Preprocess
Step 2: Train
Step 3: Validate and set the threshold
Step 4: Deploy a trained linear model
Linear learner hyperparameters
Tune a linear learner model
Metrics computed by the linear learner algorithm
Tuning linear learner hyperparameters
Linear learner response formats
JSON response formats
JSONLINES response formats
RECORDIO response formats
Neural Topic Model (NTM) Algorithm
Input/Output Interface for the NTM Algorithm
EC2 Instance Recommendation for the NTM Algorithm
NTM Sample Notebooks
NTM Hyperparameters
Tune an NTM Model
Metrics Computed by the NTM Algorithm
Tunable NTM Hyperparameters
NTM Response Formats
JSON Response Format
JSONLINES Response Format
RECORDIO Response Format
Object2Vec Algorithm
I/O Interface for the Object2Vec Algorithm
EC2 Instance Recommendation for the Object2Vec Algorithm
Instance Recommendation for Training
Instance Recommendation for Inference
Object2Vec Sample Notebooks
How Object2Vec Works
Step 1: Process Data
Step 2: Train a Model
Step 3: Produce Inferences
Object2Vec Hyperparameters
Tune an Object2Vec Model
Metrics Computed by the Object2Vec Algorithm
Regressor Metrics Computed by the Object2Vec Algorithm
Classification Metrics Computed by the Object2Vec Algorithm
Tunable Object2Vec Hyperparameters
Data Formats for Object2Vec Training
Input: JSON Lines Request Format
Data Formats for Object2Vec Inference
GPU optimization: Classification or Regression
Input: Classification or Regression Request Format
Output: Classification or Regression Response Format
Encoder Embeddings for Object2Vec
GPU optimization: Encoder Embeddings
Input: Encoder Embeddings
Output: Encoder Embeddings
Object Detection Algorithm
Input/Output Interface for the Object Detection Algorithm
Train with the RecordIO Format
Train with the Image Format
Train with Augmented Manifest Image Format
Incremental Training
EC2 Instance Recommendation for the Object Detection Algorithm
Object Detection Sample Notebooks
How Object Detection Works
Object Detection Hyperparameters
Tune an Object Detection Model
Metrics Computed by the Object Detection Algorithm
Tunable Object Detection Hyperparameters
Object Detection Request and Response Formats
Request Format
Response Formats
OUTPUT: JSON Response Format
Principal Component Analysis (PCA) Algorithm
Input/Output Interface for the PCA Algorithm
EC2 Instance Recommendation for the PCA Algorithm
PCA Sample Notebooks
How PCA Works
Mode 1: Regular
Mode 2: Randomized
PCA Hyperparameters
PCA Response Formats
JSON Response Format
JSONLINES Response Format
RECORDIO Response Format
Random Cut Forest (RCF) Algorithm
Input/Output Interface for the RCF Algorithm
Instance Recommendations for the RCF Algorithm
RCF Sample Notebooks
How RCF Works
Sample Data Randomly
Train a RCF Model and Produce Inferences
Choose Hyperparameters
References
RCF Hyperparameters
Tune an RCF Model
Metrics Computed by the RCF Algorithm
Tunable RCF Hyperparameters
RCF Response Formats
JSON Response Format
JSONLINES Response Format
RECORDIO Response Format
Semantic Segmentation Algorithm
Semantic Segmentation Sample Notebooks
Input/Output Interface for the Semantic Segmentation Algorithm
How Training Works
Training with the Augmented Manifest Format
Incremental Training
Produce Inferences
EC2 Instance Recommendation for the Semantic Segmentation Algorithm
Semantic Segmentation Hyperparameters
Tuning a Semantic Segmentation Model
Metrics Computed by the Semantic Segmentation Algorithm
Tunable Semantic Segmentation Hyperparameters
Sequence-to-Sequence Algorithm
Input/Output Interface for the Sequence-to-Sequence Algorithm
EC2 Instance Recommendation for the Sequence-to-Sequence Algorithm
Sequence-to-Sequence Sample Notebooks
How Sequence-to-Sequence Works
Sequence-to-Sequence Hyperparameters
Tune a Sequence-to-Sequence Model
Metrics Computed by the Sequence-to-Sequence Algorithm
Tunable Sequence-to-Sequence Hyperparameters
XGBoost Algorithm
Supported versions
How to Use SageMaker XGBoost
Input/Output Interface for the XGBoost Algorithm
EC2 Instance Recommendation for the XGBoost Algorithm
XGBoost Sample Notebooks
How XGBoost Works
XGBoost Hyperparameters
Tune an XGBoost Model
Evaluation Metrics Computed by the XGBoost Algorithm
Tunable XGBoost Hyperparameters
Deprecated Versions of XGBoost and their Upgrades
Upgrade XGBoost Version 0.90 to Version 1.2
Upgrade SageMaker Python SDK Version 1.x to Version 2.x
Change the image tag to 1.2-2
Change Docker Image for Boto3
Update Hyperparameters and Learning Objectives
XGBoost Version 0.72
Input/Output Interface for the XGBoost Release 0.72
EC2 Instance Recommendation for the XGBoost Release 0.72
XGBoost Release 0.72 Sample Notebooks
XGBoost Release 0.72 Hyperparameters
Tune an XGBoost Release 0.72 Model
Metrics Computed by the XGBoost Release 0.72 Algorithm
Tunable XGBoost Release 0.72 Hyperparameters
Use Reinforcement Learning with Amazon SageMaker
What are the differences between reinforcement, supervised, and unsupervised learning paradigms?
Why is Reinforcement Learning Important?
Markov Decision Process (MDP)
Key Features of Amazon SageMaker RL
Reinforcement Learning Sample Notebooks
Sample RL Workflow Using Amazon SageMaker RL
RL Environments in Amazon SageMaker
Use OpenAI Gym Interface for Environments in SageMaker RL
Use Open-Source Environments
Use Commercial Environments
Distributed Training with Amazon SageMaker RL
Hyperparameter Tuning with Amazon SageMaker RL
Manage Machine Learning with Amazon SageMaker Experiments
SageMaker Experiments Features
Organize Experiments
Track Experiments
Compare and Evaluate Experiments
Amazon SageMaker Autopilot
Create an Amazon SageMaker Experiment
View and Compare Amazon SageMaker Experiments, Trials, and Trial Components
View Experiments, Trials, and Trial Components
Compare Experiments, Trials, and Trial Components
Track and Compare Tutorial
Open the Notebook in Studio
Install the Experiments SDK and Import Modules
Transform and Track the Input Data
Create and Track an Experiment
Compare and Analyze Trials
Search Experiments Using Amazon SageMaker Studio
Search Experiments, Trials, and Trial Components
Search the SageMaker Studio Leaderboard
Search by Tag
Clean Up Amazon SageMaker Experiment Resources
Clean Up Using the Experiments SDK
Clean Up Using the Python SDK (Boto3)
Search Using the Amazon SageMaker Console and API
Sample Notebooks for Managing ML Experiments
Organize, Find, and Evaluate Training Jobs (Console)
Use Tags to Track Training Jobs (Console)
Find Training Jobs (Console)
Evaluate Models (Console)
Find and Evaluate Training Jobs (API)
Find Training Jobs (API)
Evaluate Models (API)
Get Suggestions for a Search (API)
Verify the Datasets Used by Your Training Jobs
Trace Model Lineage
Trace Model Lineage (Console)
Trace Model Lineage (API)
Amazon SageMaker Debugger
Amazon SageMaker Debugger Features
Supported Frameworks and Algorithms
Use Debugger with Custom Training Containers
Debugger Open-Source GitHub Repositories
Amazon SageMaker Debugger Architecture
Get Started with Debugger Tutorials
Debugger Tutorial Videos
Analyze, Detect, and Get Alerted on Problems with Training Runs Using Amazon SageMaker Debugger
Debug Models with Amazon SageMaker Debugger in Studio
Deep Dive on Amazon SageMaker Debugger and SageMaker Model Monitor
Debugger Example Notebooks
Debugger Example Notebooks for Profiling Training Jobs
Debugger Example Notebooks for Analyzing Model Parameters
Debugger Advanced Demos and Visualization
Train and Tune Your Models with Amazon SageMaker Experiments and Debugger
Using SageMaker Debugger to Monitor a Convolutional Autoencoder Model Training
Using SageMaker Debugger to Monitor Attentions in BERT Model Training
Using SageMaker Debugger to Visualize Class Activation Maps in Convolutional Neural Networks (CNNs)
Configure Debugger Using Amazon SageMaker Python SDK
Construct a SageMaker Estimator with Debugger
Configure Debugger Monitoring Hardware System Resource Utilization
Configure Debugger Framework Profiling
Start a Training Job with the Default System Monitoring and Framework Profiling
Start a Training Job with the Default System Monitoring and Customized Framework Profiling for Target Steps or a Target Time Range
Start a Training Job with the Default System Monitoring and Customized Framework Profiling with Different Profiling Options
Updating Debugger System Monitoring and Framework Profiling Configuration while a Training Job is Running
Configure Debugger Hook to Save Tensors
Configure Debugger Tensor Collections Using the CollectionConfig API Operation
Configure Debugger Hook to Save Tensors
Example Notebooks and Code Samples to Configure Debugger Hook
Tensor Visualization Example Notebooks
Save Tensors Using Debugger Built-in Collections
Save Tensors Using Debugger Modified Built-in Collections
Save Tensors Using Debugger Custom Collections
Configure Debugger Built-in Rules
Use Debugger Built-in Rules with the Default Parameter Settings
Use Debugger Built-in Rules with Custom Parameter Values
Example Notebooks and Code Samples to Configure Debugger Rules
Debugger Built-in Rules Example Notebooks
Debugger Built-in Rules Example Code
Use Debugger Built-in Rules with Parameter Modifications
Turn Off Debugger
Useful SageMaker Estimator Classmethods for Debugger
Configure Debugger Using Amazon SageMaker API
JSON (AWS CLI)
To configure a Debugger rule for debugging model parameters
To configure a Debugger built-in rule for profiling system and framework metrics
Update Debugger Profiling Configuration Using the UpdateTrainingJob API Operation
Add Debugger Custom Rule Configuration to the CreateTrainingJob API Operation
AWS Boto3
To configure a Debugger rule for debugging model parameters
To configure a Debugger built-in rule for profiling system and framework metrics
Update Debugger Profiling Configuration Using the UpdateTrainingJob API Operation
Add Debugger Custom Rule Configuration to the CreateTrainingJob API Operation
List of Debugger Built-in Rules
Debugger ProfilerRule
Debugger Rule
ProfilerReport
BatchSize
CPUBottleneck
GPUMemoryIncrease
IOBottleneck
LoadBalancing
LowGPUUtilization
OverallSystemUsage
MaxInitializationTime
OverallFrameworkMetrics
StepOutlier
CreateXgboostReport
DeadRelu
ExplodingTensor
PoorWeightInitialization
SaturatedActivation
VanishingGradient
WeightUpdateRatio
AllZero
ClassImbalance
LossNotDecreasing
Overfit
Overtraining
SimilarAcrossRuns
StalledTrainingRule
TensorVariance
UnchangedTensor
CheckInputImages
NLPSequenceRatio
Confusion
FeatureImportanceOverweight
TreeDepth
Create Debugger Custom Rules for Training Job Analysis
Prerequisites for Creating Debugger Custom Rules
Use the Debugger Client Library smdebug to Create a Custom Rule Python Script
Use the Debugger APIs to Run Your Own Custom Rules
Use Debugger with Custom Training Containers
Prepare to Build a Custom Training Container
Register Debugger Hook to Your Training Script
Create and Configure a Dockerfile
Build and Push the Custom Training Container to Amazon ECR
Run and Debug Training Jobs Using the Custom Training Container
Action on Amazon SageMaker Debugger Rules
Debugger Built-in Actions for Rules
Step 1: Set Up Amazon SNS, Create an SMDebugRules Topic, and Subscribe to the Topic
Step 2: Set Up Your IAM Role to Attach Required Policies
Step 3: Configure Debugger Rules with the Built-in Actions
Considerations for Using the Debugger Built-in Actions
Create Actions on Rules Using Amazon CloudWatch and AWS Lambda
CloudWatch Logs for Debugger Rules and Training Jobs
Set Up Debugger for Automated Training Job Termination Using CloudWatch and Lambda
Step 1: Create a Lambda Function
Step 2: Configure the Lambda function
Step 3: Create a CloudWatch Events Rule and Link to the Lambda Function for Debugger
Run Example Notebooks to Test Automated Training Job Termination
Disable the CloudWatch Events Rule to Stop Using the Automated Training Job Termination
SageMaker Debugger on Studio
Open Amazon SageMaker Debugger Insights Dashboard
SageMaker Debugger Insights Dashboard Controller
SageMaker Debugger Insights Controller UI
Enable and Configure Debugger Profiling for Detailed Insights
SageMaker Debugger Insights Dashboard Walkthrough
Debugger Insights – Overview
Training job summary
Resource utilization summary
Resource intensive operations
Insights
Debugger Insights – Nodes
Shut Down the SageMaker Debugger Insights Instance
SageMaker Debugger on Studio Experiments
Visualize Tensors Using SageMaker Debugger and Studio
Loss Curves While Training Is in Progress
Analyzing Training Jobs: Comparing Loss Curves Across Multiple Jobs
Rules Triggering and Logs from Jobs
SageMaker Debugger Interactive Reports
SageMaker Debugger Profiling Report
Download a Debugger Profiling Report
Debugger Profiling Report Walkthrough
Training Job Summary
System Usage Statistics
Framework metrics summary
Overview: CPU Operators
Overview: GPU Operators
Rules Summary
Analyzing the Training Loop – Step Durations
GPU Utilization Analysis
Batch Size
CPU Bottlenecks
I/O Bottlenecks
LoadBalancing in Multi-GPU Training
GPU Memory Analysis
SageMaker Debugger XGBoost Training Report
Construct a SageMaker XGBoost Estimator with the Debugger XGBoost Report Rule
Download the Debugger XGBoost Training Report
Debugger XGBoost Training Report Walkthrough
Distribution of True Labels of the Dataset
Loss versus Step Graph
Feature Importance
Confusion Matrix
Evaluation of the Confusion Matrix
Accuracy Rate of Each Diagonal Element Over Iteration
Receiver Operating Characteristic Curve
Distribution of Residuals at the Last Saved Step
Absolute Validation Error per Label Bin Over Iteration
Analyze Data Using the SMDebug Client Library
Access the Monitoring and Profiling Data
Plot the System Metrics and Framework Metrics Data
Access the Profiling Data Using the Pandas Data Parsing Tool
Access the Python Profiling Stats Data
Merge Timelines of Different Profiling Trace Files
Profiling Data Loader
Visualize Amazon SageMaker Debugger Output Tensors in TensorBoard
Best Practices for Amazon SageMaker Debugger
Choose a Machine Learning Framework
Use Studio Debugger Insights Dashboard
Download Debugger Reports and Gain More Insights
Capture Data from Your Training Job and Save Data to Amazon S3
Analyze the Data with a Fleet of Debugger Built-in Rules
Take Actions Based on the Built-in Rule Status
Dive Deep into the Data Using the SMDebug Client Library
Monitoring System Utilization and Detect Bottlenecks
Profiling Framework Operations
Debugging Model Parameters
Amazon SageMaker Debugger Advanced Topics and Reference Documentation
Amazon SageMaker Debugger API Operations
Use Debugger Docker Images for Built-in or Custom Rules
Amazon SageMaker Debugger Registry URLs for Built-in Rule Evaluators
Amazon SageMaker Debugger Registry URLs for Custom Rule Evaluators
Amazon SageMaker Debugger Exceptions
Considerations for Amazon SageMaker Debugger
Considerations for Distributed Training
Considerations for Monitoring System Bottlenecks and Profiling Framework Operations
Considerations for Debugging Model Output Tensors
Amazon SageMaker Debugger Usage Statistics
Debugger Profiling Report Usage
(Recommended) Option 1: Opt Out before Running a Training Job
Option 2: Opt Out after a Training Job Has Completed
Perform Automatic Model Tuning with SageMaker
How Hyperparameter Tuning Works
Random Search
Bayesian Search
Define Metrics
Define Hyperparameter Ranges
Hyperparameter Scaling
Tune Multiple Algorithms with Hyperparameter Optimization to Find the Best Model
Get Started
Create a Hyperparameter Optimization Tuning Job for One or More Algorithms (Console)
Define job settings
Create Training Job Definitions
Configure algorithm and parameters
Define Data Input and Output
Configure Training Job Resources
Add or Clone a Training Job
Configure Tuning Job Resources
Review and Create HPO Tuning Job
Manage Hyperparameter Tuning and Training Jobs
Example: Hyperparameter Tuning Job
Prerequisites
Create a Notebook
Next Step
Get the Amazon SageMaker Boto 3 Client
Next Step
Get the SageMaker Execution Role
Next Step
Specify a S3 Bucket to Upload Training Datasets and Store Output Data
Next Step
Download, Prepare, and Upload Training Data
Download and Explore the Training Dataset
Prepare and Upload Data
Next Step
Configure and Launch a Hyperparameter Tuning Job
Specify the Hyperparameter Tuning Job Settings
Configure the Training Jobs
Name and Launch the Hyperparameter Tuning Job
Next Step
Monitor the Progress of a Hyperparameter Tuning Job
View the Status of the Hyperparameter Tuning Job
View the Status of the Training Jobs
View the Best Training Job
Next Step
Clean up
Stop Training Jobs Early
How Early Stopping Works
Algorithms That Support Early Stopping
Run a Warm Start Hyperparameter Tuning Job
Types of Warm Start Tuning Jobs
Warm Start Tuning Restrictions
Warm Start Tuning Sample Notebook
Create a Warm Start Tuning Job
Create a Warm Start Tuning Job ( Low-level SageMaker API for Python (Boto 3))
Create a Warm Start Tuning Job (SageMaker Python SDK)
Resource Limits for Automatic Model Tuning
Best Practices for Hyperparameter Tuning
Choosing the Number of Hyperparameters
Choosing Hyperparameter Ranges
Using Logarithmic Scales for Hyperparameters
Choosing the Best Number of Concurrent Training Jobs
Running Training Jobs on Multiple Instances
Amazon SageMaker Distributed Training Libraries
Get Started with Distributed Training
Basic Distributed Training Concepts
Advanced Concepts
Strategies
Train with Data Parallel and Model Parallel
Optimize Distributed Training
Batch Size
Mini-Batch Size
Scenarios
Scaling from a Single GPU to Many GPUs
Scaling from a Single Instance to Multiple Instances
Availability Zones and Network Backplane
Optimized GPU, Network, and Storage
Custom Training Scripts
SageMaker Built-In Distributed Training Features
SageMaker's Distributed Data Parallel Library
Introduction to SageMaker's Distributed Data Parallel Library
Why Use SageMaker Distributed Data Parallel Library?
Training Benchmarks
Optimal Bandwidth Use with Balanced Fusion Buffer
Optimal GPU Usage with Efficient AllReduce Overlapping with a Backward Pass
SageMaker Distributed Data Parallel Architecture
Modify Your Training Script Using the SageMaker Data Parallel Library
Script Modification Overview
Modify a TensorFlow Training Script
Modify a PyTorch Training Script
Run a SageMaker Distributed Data Parallel Training Job
Use SageMaker's Distributed Data Parallel Library
Use the Data Parallel Library with SageMaker's Python SDK
TensorFlow Estimator
PyTorch Estimator
SageMaker distributed data parallel Configuration Tips and Pitfalls
Data Preprocessing
Single vs Multiple Nodes
Debug Scaling Efficiency with Debugger
Batch Size
Custom MPI Options
Data Parallel Library FAQ
Data Parallel Troubleshooting
Considerations for Using SageMaker Distributed Data Parallel with SageMaker Debugger and Checkpoints
An Unexpected Prefix (model for example) Is Attached to state_dict keys (model parameters) from a PyTorch Distributed Training Job
SageMaker's Distributed Model Parallel
Introduction to Model Parallelism
What is Model Parallelism?
Important Considerations when Using Model Parallelism
Core Features of SageMaker Distributed Model Parallel
Automated Model Splitting
How It Works
Automated Model Splitting with PyTorch
Automated Model Splitting with TensorFlow
Comparison of Automated Model Splitting Between Frameworks
Manual Model Splitting
Pipeline Execution Schedule
Interleaved Pipeline
Simple Pipeline
Pipelining Execution in Specific Frameworks
Pipeline Execution with TensorFlow
Pipeline Execution with PyTorch
Modify Your Training Script Using SageMaker's Distributed Model Parallel Library
Modify a TensorFlow Training Script
Unsupported Framework Features
TensorFlow
TensorFlow with Horovod
Manual partitioning with TensorFlow
Modify a PyTorch Training Script
Important Considerations
Unsupported Framework Features
PyTorch
Manual Partitioning with PyTorch
Run a SageMaker Distributed Model Parallel Training Job
Launch a Training Job with the SageMaker Python SDK
Extend or Adapt A Docker Container that Contains SageMaker's Distributed Model Parallel Library
SageMaker distributed model parallel Configuration Tips and Pitfalls
Model Parallel Troubleshooting
Considerations for Using SageMaker Debugger with SageMaker Distributed Model Parallel
Saving Checkpoints
Convergence Using Model Parallel and TensorFlow
Amazon SageMaker Distributed Training Notebook Examples
Blogs and Case Studies
PyTorch Examples
TensorFlow Examples
HuggingFace Examples
How to Access or Download the SageMaker Distributed Training Notebook Examples
Option 1: Use a SageMaker notebook instance
Option 2: Clone the SageMaker example repository to SageMaker Studio or notebook instance
Detect Posttraining Data and Model Bias
Sample Notebooks
Measure Posttraining Data and Model Bias
Difference in Positive Proportions in Predicted Labels (DPPL)
Disparate Impact (DI)
Difference in Conditional Acceptance (DCAcc)
Difference in Conditional Rejection (DCR)
Recall Difference (RD)
Difference in Acceptance Rates (DAR)
Difference in Rejection Rates (DRR)
Accuracy Difference (AD)
Treatment Equality (TE)
Conditional Demographic Disparity in Predicted Labels (CDDPL)
Counterfactual Fliptest (FT)
Configure an Amazon SageMaker Clarify Processing Jobs for Fairness and Explainability
Prerequisites
Getting Started with a SageMaker Clarify Container
How It Works
Configure a Processing Job Container's Input and Output Parameters
Configure the Analysis
Example Analysis Configuration JSON File for a CSV Dataset
Example Analysis Configuration JSON File for a JSONLines Dataset
Run SageMaker Clarify Processing Jobs for Bias Analysis and Explainability
Compute Resources Required for SageMaker Clarify Processing Jobs
Run the Clarify Processing Job
Run the Clarify Processing Job with Spark
Get the Analysis Results
Troubleshoot SageMaker Clarify Processing Jobs
Processing job fails to finish
Processing job finishes without results and you get a CloudWatch warning message
Error message for invalid analysis configuration
Bias metric computation fails for several or all metrics
Mismatch between analysis config and dataset/model input/output
Model returns 500 Internal Server Error or container falls back to per-record predictions due to model error
Execution role is invalid
Failed to download data
Could not connect to SageMaker
Model Explainability
Feature Attributions that Use Shapley Values
SHAP Baselines for Explainability
Create Feature Attribute Baselines and Explainability Reports
Incremental Training in Amazon SageMaker
Perform Incremental Training (Console)
Perform Incremental Training (API)
Managed Spot Training in Amazon SageMaker
Using Managed Spot Training
Managed Spot Training Lifecycle
Use Checkpoints in Amazon SageMaker
Checkpoints for Frameworks and Algorithms in SageMaker
Enable Checkpointing
Browse Checkpoint Files
Resume Training From a Checkpoint
Considerations for Checkpointing
Provide Dataset Metadata to Training Jobs with an Augmented Manifest File
Augmented Manifest File Format
Stream Augmented Manifest File Data
Use an Augmented Manifest File (Console)
Use an Augmented Manifest File (API)
Monitor and Analyze Training Jobs Using Metrics
Training Metrics Sample Notebooks
Defining Training Metrics
Defining Regular Expressions for Metrics
Defining Training Metrics (Low-level SageMaker API)
Defining Training Metrics (SageMaker Python SDK)
Define Training Metrics (Console)
Monitoring Training Job Metrics ( Console)
Monitoring Training Job Metrics (SageMaker Console)
Example: Viewing a Training and Validation Curve
Deploy Models for Inference
Prerequisites
What do you want to do?
Manage Model Deployments
Deploy Your Own Inference Code
Guide to SageMaker
Use Amazon SageMaker Elastic Inference (EI)
How EI Works
Choose an EI Accelerator Type
Use EI in a SageMaker Notebook Instance
Use EI on a Hosted Endpoint
Frameworks that Support EI
Use EI with SageMaker Built-in Algorithms
EI Sample Notebooks
Set Up to Use EI
Set Up Required Permissions
Use a Custom VPC to Connect to EI
Set up Security Groups to Connect to EI
Set up a VPC Interface Endpoint to Connect to EI
Attach EI to a Notebook Instance
Set Up to Use EI
Use EI in Local Mode in SageMaker
Use EI in Local Mode with SageMaker TensorFlow Estimators and Models
Use EI in Local Mode with SageMaker Apache MXNet Estimators and Models
Use EI in Local Mode with SageMaker PyTorch Estimators and Models
Use EI on Amazon SageMaker Hosted Endpoints
Use EI with a SageMaker TensorFlow Container
Use an Estimator Object
Use a Model Object
Use EI with a SageMaker MXNet Container
Use an Estimator Object
Use a Model Object
Use EI with a SageMaker PyTorch Container
Use an Estimator Object
Use a Model Object
Use EI with Your Own Container
Import the EI Version of TensorFlow, MXNet, or PyTorch into Your Docker Container
Create an EI Endpoint with AWS SDK for Python (Boto 3)
Create an Endpoint Configuration
Create an Endpoint
Asynchronous Inference
How It Works
How Do I Get Started?
Create, Invoke, and Update an Asynchronous Endpoint
Prerequisites
Create an Asynchronous Inference Endpoint
Create a Model
Create an Endpoint Configuration
Create Endpoint
Invoke an Asynchronous Endpoint
Update an Asynchronous Endpoint
Delete an Asynchronous Endpoint
Monitor Asynchronous Endpoint
Monitoring with CloudWatch
Common Endpoint Metrics
Asynchronous Inference Endpoint Metrics
Logs
Check Prediction Results
Amazon SNS Topics
Check Your S3 Bucket
Autoscale an Asynchronous Endpoint
Define a Scaling Policy
Define a Scaling Policy that Scales to 0
Use Batch Transform
Use Batch Transform to Get Inferences from Large Datasets
Speed up a Batch Transform Job
Use Batch Transform to Test Production Variants
Batch Transform Errors
Batch Transform Sample Notebooks
Associate Prediction Results with Input Records
Workflow for Associating Inferences with Input Records
Use Data Processing in Batch Transform Jobs
Supported JSONPath Operators
Batch Transform Examples
Example: Output Only Inferences
Example: Output Input Data and Inferences
Example: Output an ID Column with Results and Exclude the ID Column from the Input (CSV)
Example: Output an ID Attribute with Results and Exclude the ID Attribute from the Input (JSON)
Host Multiple Models with Multi-Model Endpoints
Supported Algorithms and Frameworks
Sample Notebooks for Multi-Model Endpoints
How Multi-Model Endpoints Work
Setting SageMaker Multi-Model Endpoint Model Caching Behavior
Instance Recommendations for Multi-Model Endpoint Deployments
Create a Multi-Model Endpoint
Create a Multi-Model Endpoint (Console)
Create a Multi-Model Endpoint (AWS SDK for Python (Boto))
Invoke a Multi-Model Endpoint
Retry Requests on ModelNotReadyException Errors
Add or Remove Models
Build Your Own Container with Multi Model Server
Use the SageMaker Inference Toolkit
Contract for Custom Containers to Serve Multiple Models
Load Model API
List Model API
Get Model API
Unload Model API
Invoke Model API
Multi-Model Endpoint Security
CloudWatch Metrics for Multi-Model Endpoint Deployments
Deploy multi-container endpoints
Create a multi-container endpoint (Boto 3)
Update a multi-container endpoint
Delete a multi-container endpoint
Use a multi-container endpoint with direct invocation
Invoke a multi-container endpoint with direct invocation
Security with multi-container endpoints with direct invocation
Metrics for multi-container endpoints with direct invocation
Autoscale multi-container endpoints
Troubleshoot multi-container endpoints
Ping Health Check Errors
Missing accept-bind-to-port=true Docker label
Deploy an Inference Pipeline
Sample Notebooks for Inference Pipelines
Feature Processing with Spark ML and Scikit-learn
Feature Processing with Spark ML
Feature Processing with Sci-kit Learn
Create a Pipeline Model
Run Real-time Predictions with an Inference Pipeline
Create and Deploy an Inference Pipeline Endpoint
Request Real-Time Inference from an Inference Pipeline Endpoint
Realtime inference pipeline example
Run Batch Transforms with Inference Pipelines
Inference Pipeline Logs and Metrics
Use Metrics to Monitor Multi-container Models
Use Logs to Monitor an Inference Pipeline
Troubleshoot Inference Pipelines
Troubleshoot Amazon ECR Permissions for Inference Pipelines
Use CloudWatch Logs to Troubleshoot SageMaker Inference Pipelines
Use Error Messages to Troubleshoot Inference Pipelines
Automatically Scale Amazon SageMaker Models
Prerequisites
Autoscaling policy overview
Target metric for autoscaling
Minimum and maximum capacity
Cooldown period
Permissions
Service-linked role
Configure model autoscaling with the console
Register a model
Register a model with the AWS CLI
Register a model with the Application Auto Scaling API
Define a scaling policy
Use a predefined metric
Use a custom metric
Add a cooldown period
Apply a scaling policy
Apply a scaling policy (AWS CLI)
Apply a scaling policy (Application Auto Scaling API)
Edit a scaling policy
Scale-in
Disable scale-in activity
Scale-out
Disable scale-out activity
Edit a scaling policy (Console)
Edit a scaling policy (AWS CLI or Application Auto Scaling API)
Delete a scaling policy
Delete a scaling policy (Console)
Delete a scaling policy (AWS CLI or Application Auto Scaling API)
Delete a scaling policy (AWS CLI)
Delete a scaling policy (Application Auto Scaling API)
Query Endpoint Autoscaling History
How To Query Endpoint Autoscaling Actions
How to Identify Blocked AutoScaling Due to Instance Quotas
Update or delete endpoints that use automatic scaling
Update endpoints that use automatic scaling
Delete endpoints configured for automatic scaling
Load testing your autoscaling configuration
Determine the performance characteristics
Calculate the target load
Use AWS CloudFormation to update autoscaling policies
Host Instance Storage Volumes
Test models in production
Test models by specifying traffic distribution
Test models by invoking specific variants
Model A/B test example
Step 1: Create and deploy models
Step 2: Invoke the deployed models
Step 3: Evaluate model performance
Step 4: Increase traffic to the best model
Troubleshoot Amazon SageMaker Model Deployments
Detection Errors in the Active CPU Count
Deployment Best Practices
Deploy Multiple Instances Across Availability Zones
Amazon SageMaker Model Monitor
How Model Monitor Works
Model Monitor Sample Notebooks
Monitor Data Quality
Create a Baseline
Schema for Statistics (statistics.json file)
CloudWatch Metrics
Schema for Violations (constraint_violations.json file)
Monitor Model Quality
Create a Model Quality Baseline
Schedule Model Quality Monitoring Jobs
Ingest Ground Truth Labels and Merge Them With Predictions
Model Quality Metrics
Regression Metrics
Binary Classification Metrics
Multiclass Metrics
Model Quality CloudWatch Metrics
Monitor Bias Drift for Models in Production
Model Monitor Sample Notebook
Create a Bias Drift Baseline
Schedule Bias Drift Monitoring Jobs
Inspect Reports for Data Bias Drift
Monitor Feature Attribution Drift for Models in Production
Model Monitor Example Notebook
Create a SHAP Baseline for Models in Production
Schedule Feature Attribute Drift Monitoring Jobs
Inspect Reports for Feature Attribute Drift in Production Models
Capture Data
Schedule Monitoring Jobs
The cron Expression for Monitoring Schedule
Amazon SageMaker Model Monitor Prebuilt Container
Interpret Results
List Executions
Inspect a Specific Execution
List Generated Reports
Violations Report
Visualize Results in Amazon SageMaker Studio
Advanced Topics
Customize Monitoring
Preprocessing and Postprocessing
Postprocessing Script
Preprocessing Script
Bring Your Own Containers
Container Contract Inputs
Container Contract Outputs
Schema for Statistics (statistics.json file)
Schema for Constraints (constraints.json file)
CloudWatch Metrics for Bring Your Own Containers
Create a Monitoring Schedule with an AWS CloudFormation Custom Resource
Custom Resource
Lambda Custom Resource Code
Register and Deploy Models with Model Registry
Create a Model Group
Create a Model Package Group (Boto3)
Create a Model Package Group (SageMaker Studio)
Register a Model Version
Register a Model Version (SageMaker Pipelines)
Register a Model Version (Boto3)
View Model Groups and Versions
View a List of Model Versions in a Group
View a List of Model Versions in a Group (Boto3)
View a List of Model Versions in a Group (SageMaker Studio)
View the Details of a Model Version
View the Details of a Model Version (Boto3)
View the Details of a Model Version (SageMaker Studio)
Update the Approval Status of a Model
Update the Approval Status of a Model (Boto3)
Update the Approval Status of a Model (SageMaker Studio)
Deploy a Model in the Registry
Deploy a Model in the Registry (SageMaker SDK)
Deploy a Model in the Registry (Boto3)
Deploy a Model Version from a Different Account
View the Deployment History of a Model
Compile and Deploy Models with Neo
What is SageMaker Neo?
How it Works
Neo Sample Notebooks
Use Neo to Compile a Model
Prepare Model for Compilation
What input data shapes does SageMaker Neo expect?
Keras
MXNet/ONNX
PyTorch
TensorFlow
TFLite
XGBoost
Saving Models for SageMaker Neo
Keras
MXNet
PyTorch
TensorFlow
Built-In Estimators
Compile a Model (AWS Command Line Interface)
Compile a Model (Amazon SageMaker Console)
Compile a Model (Amazon SageMaker SDK)
Cloud Instances
Supported Instance Types and Frameworks
Cloud Instances
Instance Types
AWS Inferentia
Amazon Elastic Inference
Deploy a Model
Prerequisites
Deploy a Compiled Model Using SageMaker SDK
If you compiled your model using the SageMaker SDK
If you compiled your model using MXNet or PyTorch
If you compiled your model using Boto3, SageMaker console, or the CLI for TensorFlow
Deploy a Compiled Model Using Boto3
Deploy the Model
Deploy a Compiled Model Using the AWS CLI
Deploy the Model
Create a Model
Create an Endpoint Configuration
Create an Endpoint
Deploy a Compiled Model Using the Console
Deploy the Model
Request Inferences from a Deployed Service
Request Inferences from a Deployed Service (Amazon SageMaker SDK)
PyTorch and MXNet
TensorFlow
Request Inferences from a Deployed Service (Boto3)
Request Inferences from a Deployed Service (AWS CLI)
Inference Container Images
Amazon SageMaker XGBoost
TensorFlow
MXNet
PyTorch
Edge Devices
Supported Frameworks, Devices, Systems, and Architectures
Supported Frameworks
Supported Devices, Chip Architectures, and Systems
Devices
Systems and Chip Architectures
Tested Models
DarkNet
MXNet
Keras
ONNX
PyTorch (FP32)
TensorFlow
TensorFlow-Lite
Deploy Models
Deploy a Compiled Model (DLR)
Deploy a Model (AWS IoT Greengrass)
Getting Started with Neo on Edge Devices
Prerequisites
Step 1: Compile the Model
Step 2: Set Up Your Device
Step 3: Make Inferences on Your Device
Troubleshoot Errors
Error Classification Types
Client permission error
Load error
Compilation error
Troubleshoot Neo Compilation Errors
How to Use This Page
Framework-Related Errors
TensorFlow
Keras
MXNet
Infrastructure-Related Errors
Troubleshoot Neo Inference Errors
Troubleshoot Ambarella Errors
Setting up the Configuration File
Calibration Images
Mean and Scale
SageMaker Edge Manager
Why Use Edge Manager?
How Does it Work?
How Do I Use SageMaker Edge Manager?
Getting Started
Setting Up
Train, Compile, and Package Your Model
Create and Register Fleets and Authenticate Devices
Download and Set Up Edge Manager
Run Agent
Set Up Devices and Fleets
Create a Fleet
Create a Fleet (Boto3)
Create a Fleet (Console)
Register a Device
Register a Device (Boto3)
Register a Device (Console)
Check Status
Package Model
Prerequisites
Package a Model (Amazon SageMaker Console)
Package a Model (Boto3)
Edge Manager Agent
Download and Set Up Edge Manager Agent Manually
How the Agent Works
Installing Edge Manager Agent
Running SageMaker Edge Manager Agent
Deploy Model Package and Edge Manager Agent with AWS IoT Greengrass
Prerequisites
Create AWS IoT Greengrass V2 Components
Autogenerated Component
Create a Hello World custom component
Deploy Components to Your Device
To deploy your components (console)
To deploy your components (AWS CLI)
Manage Model
Load Model
Unload Model
List Models
Describe Model
Capture Data
Get Capture Status
Predict
Using Docker containers with SageMaker
Scenarios for Running Scripts, Training Algorithms, or Deploying Models with SageMaker
Docker Container Basics
Use Prebuilt SageMaker Docker images
Prebuilt SageMaker Docker Images for Deep Learning
Using the SageMaker Python SDK
Extending Prebuilt SageMaker Docker Images
Prebuilt Amazon SageMaker Docker Images for Scikit-learn and Spark ML
Using the SageMaker Python SDK
Specifying the Prebuilt Images Manually
Finding Available Images
Train a Deep Graph Network
What Is a Deep Graph Network?
Get Started
Run a Graph Network Training Example
Examples
Use a Deep Learning Container with DGL
Bring Your Own Container with DGL
Extend a Prebuilt Container
Requirements to Extend a Prebuilt Container
Extend SageMaker Containers to Run a Python Script
Step 1: Create an SageMaker Notebook Instance
Step 2: Create and Upload the Dockerfile and Python Training Scripts
Step 3: Build the Container
Step 4: Test the Container
Step 5: Push the Container to Amazon Elastic Container Registry (Amazon ECR)
Step 6: Clean up Resources
Adapting Your Own Docker Container to Work with SageMaker
Individual Framework Libraries
Using the SageMaker Training and Inference Toolkits
SageMaker Toolkits Containers Structure
Single Versus Multiple Containers
Adapting Your Own Training Container
Step 1: Create a SageMaker notebook instance
Step 2: Create and upload the Dockerfile and Python training scripts
Step 3: Build the container
Step 4: Test the container
Step 5: Push the container to Amazon Elastic Container Registry (Amazon ECR)
Step 6: Clean up resources
Adapting Your Own Inference Container
Step 1: Create an Inference Handler
The model_fn Function
The input_fn Function
The predict_fn Function
The output_fn Function
Step 2: Implement a Handler Service
Step 3: Implement an Entrypoint
Step 4: Write a Dockerfile
Step 5: Build and Register Your Container
Create a container with your own algorithms and models
Use Your Own Training Algorithms
How Amazon SageMaker Runs Your Training Image
How Amazon SageMaker Provides Training Information
Hyperparameters
Environment Variables
Input Data Configuration
Training Data
Distributed Training Configuration
Run Training with EFA
Prerequisites
Install EFA and required packages
Considerations when creating your container
Verify that your EFA device is recognized
Running a training job with EFA
How Amazon SageMaker Signals Algorithm Success and Failure
How Amazon SageMaker Processes Training Output
Use Your Own Inference Code
Use Your Own Inference Code with Hosting Services
How SageMaker Runs Your Inference Image
How SageMaker Loads Your Model Artifacts
How Containers Serve Requests
How Your Container Should Respond to Inference Requests
How Your Container Should Respond to Health Check (Ping) Requests
Use a Private Docker Registry for Real-Time Inference Containers
Store Images in a Private Docker Registry other than Amazon Elastic Container Registry
Use an Image from a Private Docker Registry for Real-time Inference
Allow SageMaker to authenticate to a private Docker registry
Create the Lambda function
Give your execution role permission to Lambda
Create an interface VPC endpoint for Lambda
Use Your Own Inference Code with Batch Transform
How SageMaker Runs Your Inference Image
How SageMaker Loads Your Model Artifacts
How Containers Serve Requests
How Your Container Should Respond to Inference Requests
How Your Container Should Respond to Health Check (Ping) Requests
Example Notebooks: Use Your Own Algorithm or Model
Setup
Host Models Trained in Scikit-learn
Package TensorFlow and Scitkit-learn Models for Use in SageMaker
Train and Deploy a Neural Network on SageMaker
Training Using Pipe Mode
Bring Your Own R Model
Extend a Prebuilt PyTorch Container Image
Train and Debug Training Jobs on a Custom Container
Troubleshooting your Docker containers
SageMaker Workflows
Amazon SageMaker Model Building Pipelines
SageMaker Pipelines Overview
Pipeline Structure
Access Management
Pipeline Role Permissions
Pipeline Step Permissions
Service Control Policies with Pipelines
Pipeline Parameters
Pipeline Steps
Step Types
Processing Step
Training Step
Tuning Step
CreateModel Step
RegisterModel Step
Transform Step
Condition Step
Callback Step
Lambda Step
Step Properties
Data Dependency Between Steps
Custom Dependency Between Steps
Use a Custom Image in a Step
Property Files and JsonGet
Caching Pipeline Steps
Enabling Step Caching
Amazon EventBridge Integration
Schedule a Pipeline with Amazon EventBridge
Prerequisites
Create an EventBridge rule using the EventBridge console
Create an EventBridge rule using the AWS CLI
Amazon SageMaker Experiments Integration
Default Behavior
Disable Experiments Integration
Specify a Custom Experiment Name
Specify a Custom Trial Name
SageMaker Pipelines Quotas
Pipeline Quotas
Executions Quotas
Step Quotas
Parameters Quotas
Condition Step Quotas
Property Files Quotas
Metadata Quotas
Troubleshooting Amazon SageMaker Model Building Pipelines
Create and Manage SageMaker Pipelines
Define a Pipeline
Prerequisites
Set Up Your Environment
Create a Pipeline
Step 1: Download the Dataset
Step 2: Define Pipeline Parameters
Step 3: Define a Processing Step for Feature Engineering
Step 4: Define a Training step
Step 5: Define a Processing Step for Model Evaluation
Step 6: Define a CreateModelStep for Batch Transformation
Step 7: Define a TransformStep to Perform Batch Transformation
Step 8: Define a RegisterModel Step to Create a Model Package
Step 9: Define a Condition Step to Verify Model Accuracy
Step 10: Create a pipeline
Run a pipeline
Prerequisites
Step 1: Start the Pipeline
Step 2: Examine a Pipeline Execution
Step 3: Override Default Parameters for a Pipeline Execution
Step 4: Stop and Delete a Pipeline Execution
View, Track, and Execute SageMaker Pipelines in SageMaker Studio
View a Pipeline
View a Pipeline Execution
View Experiment Entities Created by SageMaker Pipelines
Execute a Pipeline
Track the Lineage of a SageMaker ML Pipeline
Automate MLOps with SageMaker Projects
What is a SageMaker Project?
When Should You Use a SageMaker Project?
Do I Need to Create a Project to Use SageMaker Pipelines?
Why Should You Use MLOps?
Challenges with MLOps
Benefits of MLOps
SageMaker Studio Permissions Required to Use Projects
Create an MLOps Project using Amazon SageMaker Studio
MLOps Project Templates
Use SageMaker Provided Project Templates
MLOps template for model building, training, and deployment
MLOps template for model building, training, and deployment with third-party Git repositories using CodePipeline
MLOps template for model building, training, and deployment with third-party Git repositories using Jenkins
Update SageMaker Projects to Use Third-Party Git Repositories
Create Custom Project Templates
View Project Resources
SageMaker MLOps Project Walkthrough
Step 1: Create the Project
Step 2: Clone the Code Repository
Step 3: Make a Change in the Code
Step 4: Approve the Model
(Optional) Step 5: Deploy the Model Version to Production
Step 6: Cleanup Resources
Amazon SageMaker ML Lineage Tracking
Tracking Entities
Amazon SageMaker Created Tracking Entities
Tracking Entities for SageMaker Jobs
Tracking Entities for Model Packages
Tracking Entities for Endpoints
Manually Create Tracking Entities
Manually Create Entities
Manually Track a Workflow
Limits
Kubernetes Orchestration
SageMaker Operators for Kubernetes
What is an operator?
Prerequisites
Permissions overview
IAM role-based setup and operator deployment
Cluster-scoped deployment
Create an OpenID Connect Provider for Your Cluster
Get the OIDC ID
Create an IAM Role
Attach the AmazonSageMakerFullAccess Policy to the Role
Deploy the Operator
Deploy the Operator Using YAML
Deploy the Operator Using Helm Charts
Verify the operator deployment
Namespace-scoped deployment
Create an OpenID Connect Provider for Your Amazon EKS cluster
Get your OIDC ID
Create your IAM Role
Attach the AmazonSageMakerFullAccess Policy to your Role
Deploy the Operator to Your Namespace
Deploy the Operator to Your Namespace Using YAML
Deploy the Operator to Your Namespace Using Helm Charts
Verify the operator deployment to your namespace
Install the SageMaker logs kubectl plugin
Delete operators
Delete cluster-based operators
Operators installed using YAML
Operators installed using Helm Charts
Delete namespace-based operators
Operators installed with YAML
Operators installed with Helm Charts
Troubleshooting
Debugging a Failed Job
Deleting an Operator CRD
Images and SMlogs in each Region
Using Amazon SageMaker Jobs
TrainingJob operator
Create a TrainingJob Using a YAML File
Create a TrainingJob Using a Helm Chart
Create the TrainingJob
Verify Your Training Helm Chart
List TrainingJobs
TrainingJob Status Values
Secondary Status Values
Describe a TrainingJob
View Logs from TrainingJobs
Delete TrainingJobs
HyperParameterTuningJob operator
Create a HyperparameterTuningJob Using a YAML File
Create a HyperparameterTuningJob using a Helm Chart
Create the HyperparameterTuningJob
Verify Chart Installation
List HyperparameterTuningJobs
Hyperparameter Tuning Job Status Values
Status Counters
Best TrainingJob
Spawned TrainingJobs
Describe a HyperparameterTuningJob
View Logs from HyperparameterTuningJobs
Delete a HyperparameterTuningJob
BatchTransformJob operator
Create a BatchTransformJob Using a YAML File
Create a BatchTransformJob Using a Helm Chart
Get the Helm installer directory
Configure the Helm Chart
Create a BatchTransformJob
List BatchTransformJobs
Batch Transform Status Values
Describe a BatchTransformJob
View Logs from BatchTransformJobs
Delete a BatchTransformJob
HostingDeployment operator
Configure a HostingDeployment Resource
Create a HostingDeployment
List HostingDeployments
HostingDeployment Status Values
Describe a HostingDeployment
Invoking the Endpoint
Update HostingDeployment
Delete the HostingDeployment
ProcessingJob operator
Create a ProcessingJob Using a YAML File
List ProcessingJobs
Describe a ProcessingJob
Delete a ProcessingJob
HostingAutoscalingPolicy (HAP) Operator
Create a HostingAutoscalingPolicy Using a YAML File
Sample 1: Apply a Predefined Metric to a Single Endpoint Variant
Sample 2: Apply a Custom Metric to a Single Endpoint Variant
Sample 3: Apply a Scaling Policy to Multiple Endpoints and Variants
Considerations for HostingAutoscalingPolicies for Multiple Endpoints and Variants
List HostingAutoscalingPolicies
Describe a HostingAutoscalingPolicy
Update a HostingAutoscalingPolicy
Delete a HostingAutoscalingPolicy
Update or Delete an Endpoint with a HostingAutoscalingPolicy
SageMaker Components for Kubeflow Pipelines
What is Kubeflow Pipelines?
Kubeflow Pipeline components
What do SageMaker Components for Kubeflow Pipelines provide?
Training components
Inference components
Ground Truth components
IAM permissions
Converting Pipelines to use SageMaker
Using SageMaker Components
Setup
Set up a gateway node
Set up an Amazon EKS cluster
Install Kubeflow Pipelines
Access the KFP UI
Set up port forwarding to the KFP UI service
Access the KFP UI service
Create IAM Users/Roles for KFP pods and the SageMaker service
Create a KFP execution role
Create an SageMaker execution role
Add access to additional IAM users or roles
Running the Kubeflow Pipeline
Prepare datasets
Create a Kubeflow Pipeline using SageMaker Components
Input Parameters
Compile and deploy your pipeline
Install KFP SDK
Compile your pipeline
Upload and run the pipeline using the KFP CLI
Upload and run the pipeline using the KFP UI
Running predictions
Configure permissions to run predictions
Run predictions
View results and logs
Cleanup
Using Amazon Augmented AI for Human Review
Get Started with Amazon Augmented AI
Core Components of Amazon A2I
Task Types
Human Review Workflow (Flow Definition)
Human Loops
Prerequisites to Using Augmented AI
Tutorial: Get Started in the Amazon A2I Console
Prerequisites
Step 1: Create a Work Team
Step 2: Create a Human Review Workflow
Step 3: Start a Human Loop
Step 4: View Human Loop Status in Console
Step 5: Download Output Data
Tutorial: Get Started Using the Amazon A2I API
Create a Private Work Team
Create a Human Review Workflow
Create a Human Task UI
Create JSON to specify activation conditions
Create a human review workflow
Create a Human Loop
Use Cases and Examples Using Amazon A2I
Use SageMaker Notebook Instance with Amazon A2I Jupyter Notebook
Use Amazon Augmented AI with Amazon Textract
Get Started: Integrate a Human Review into an Amazon Textract Analyze Document Job
End-to-End Example Using Amazon Textract and Amazon A2I
A2I Textract Worker Console Preview
Use Amazon Augmented AI with Amazon Rekognition
Get Started: Integrate a Human Review into an Amazon Rekognition Image Moderation Job
End-to-end Demo Using Amazon Rekognition and Amazon A2I
A2I Rekognition Worker Console Preview
Use Amazon Augmented AI with Custom Task Types
End-to-end Tutorial Using Amazon A2I Custom Task Types
Create a Human Review Workflow
Create a Human Review Workflow (Console)
Next Steps
Create a Human Review Workflow (API)
Next Steps
JSON Schema for Human Loop Activation Conditions in Amazon Augmented AI
Use Human Loop Activation Conditions JSON Schema with Amazon Textract
ImportantFormKeyConfidenceCheck Inputs and Results
MissingImportantFormKey Inputs and Results
Sampling Inputs and Results
Examples
Use Human Loop Activation Conditions JSON Schema with Amazon Rekognition
ModerationLabelConfidenceCheck Inputs
Sampling Inputs
Examples
Delete a Human Review Workflow
Delete a Flow Definition Using the Console or the SageMaker API
Create and Start a Human Loop
Create and Start a Human Loop for a Built-in Task Type
Create an Amazon Textract Human Loop
Create an Amazon Rekognition Human Loop
Create and Start a Human Loop for a Custom Task Type
Next Steps:
Delete a Human Loop
Human Loop Data Retention and Deletion
Stop and Delete a Flow Definition Using the Console or the Amazon A2I API
Create and Manage Worker Task Templates
Create and Delete Worker Task Templates
Create a Worker Task Template
Delete a Worker Task Template
Create Custom Worker Task Templates
Develop Templates Locally
Use External Assets
Track Your Variables
Custom Template Example for Amazon Textract
Custom Template Example for Amazon Rekognition
Add Automation with Liquid
Use Variable Filters
Autoescape and Explicit Escape
escape_once
skip_autoescape
to_json
grant_read_access
Preview a Worker Task Template
Creating Good Worker Instructions
Create Good Worker Instructions
Add Example Images to Your Instructions
Monitor and Manage Your Human Loop
Amazon A2I Output Data
Output Data From Built-In Task Types
Output Data From Custom Task Types
Track Worker Activity
Permissions and Security in Amazon Augmented AI
CORS Permission Requirement
Add Permissions to the IAM Role Used to Create a Flow Definition
Create an IAM User That Can Invoke Amazon A2I API Operations
Create an IAM User With Permissions to Invoke Amazon A2I, Amazon Textract, and Amazon Rekognition API Operations
Enable Worker Task Template Previews
Using Amazon A2I with AWS KMS Encrypted Buckets
Additional Permissions and Security Resources
Use Amazon CloudWatch Events in Amazon Augmented AI
Send Events from Your Human Loop to CloudWatch Events
Set Up a Target to Process Events
Use Human Review Output
More Information
Use APIs in Amazon Augmented AI
Programmatic Tutorials
Buy and Sell Amazon SageMaker Algorithms and Models in AWS Marketplace
Topics
SageMaker Algorithms
SageMaker Model Packages
Use your own algorithms and models with the AWS Marketplace
Create Algorithm and Model Package Resources
Create an Algorithm Resource
Create an Algorithm Resource (Console)
Create an Algorithm Resource (API)
Create a Model Package Resource
Create a Model Package Resource (Console)
Create a Model Package Resource (API)
Use Algorithm and Model Package Resources
Use an Algorithm to Run a Training Job
Use an Algorithm to Run a Training Job (Console)
Use an Algorithm to Run a Training Job (API)
Use an Algorithm to Run a Training Job (Amazon SageMaker Python SDK)
Use an Algorithm to Run a Hyperparameter Tuning Job
Use an Algorithm to Run a Hyperparameter Tuning Job (Console)
Use an Algorithm to Run a Hyperparameter Tuning Job (API)
Use an Algorithm to Run a Hyperparameter Tuning Job (Amazon SageMaker Python SDK)
Use a Model Package to Create a Model
Use a Model Package to Create a Model (Console)
Use a Model Package to Create a Model (API)
Use a Model Package to Create a Model (Amazon SageMaker Python SDK)
Sell Amazon SageMaker Algorithms and Model Packages
Topics
Develop Algorithms and Models in Amazon SageMaker
Develop Algorithms in SageMaker
Develop Models in SageMaker
List Your Algorithm or Model Package on AWS Marketplace
Find and Subscribe to Algorithms and Model Packages on AWS Marketplace
Use Algorithms and Model Packages
Security in Amazon SageMaker
Access Control
Access control and SageMaker Studio notebooks
Control root access to a SageMaker notebook instance
Data Protection in Amazon SageMaker
Protect Data at Rest Using Encryption
Studio notebooks
Notebook instances and SageMaker jobs
Protecting Data in Transit with Encryption
Protect Communications Between ML Compute Instances in a Distributed Training Job
Enable Inter-Container Traffic Encryption (API)
Enable Inter-Container Traffic Encryption (Console)
Enable Inter-container Traffic Encryption in a Training Job
Enable Inter-container Traffic Encryption in a Hyperparameter Tuning Job
Key Management
Internetwork Traffic Privacy
Identity and Access Management for Amazon SageMaker
Audience
Authenticating with Identities
AWS account root user
IAM Users and Groups
IAM Roles
Managing Access Using Policies
Identity-Based Policies
Resource-Based Policies
Access Control Lists (ACLs)
Other Policy Types
Multiple Policy Types
How Amazon SageMaker Works with IAM
SageMaker Identity-Based Policies
Actions
Resources
Condition Keys
Examples
SageMaker Resource-Based Policies
Authorization Based on SageMaker Tags
SageMaker IAM Roles
Using Temporary Credentials with SageMaker
Service-Linked Roles
Service Roles
Choosing an IAM Role in SageMaker
Amazon SageMaker Identity-Based Policy Examples
Policy Best Practices
Using the SageMaker Console
Permissions Required to Use the Amazon SageMaker Console
Permissions Required to Use the Amazon SageMaker Ground Truth Console
Permissions Required to Use the Amazon Augmented AI (Preview) Console
Allow Users to View Their Own Permissions
Control Creation of SageMaker Resources with Condition Keys
Control Access to SageMaker Resources by Using File System Condition Keys
Restrict an IAM User to Specific Directories and Access Modes
Restrict an IAM User to a Specific File System
Restrict Training to a Specific VPC
Restrict Access to Workforce Types for Ground Truth Labeling Jobs and Amazon A2I Human Review Workflows
Enforce Encryption of Input Data
Enforce Encryption of Notebook Instance Storage Volume
Enforce Network Isolation for Training Jobs
Enforce a Specific Instance Type for Training Jobs
Enforce a Specific EI Accelerator for Training Jobs
Enforce Disabling Internet Access and Root Access for Creating Notebook Instances
Control Access to the SageMaker API by Using Identity-based Policies
Restrict Access to SageMaker API and Runtime to Calls from Within Your VPC
Limit Access to SageMaker API and Runtime Calls by IP Address
Limit Access to a Notebook Instance by IP Address
Control Access to SageMaker Resources by Using Tags
Require the Presence or Absence of Tags for API Calls
SageMaker Roles
Get execution role
Add Additional Amazon S3 Permissions to an SageMaker Execution Role
Passing Roles
CreateAutoMLJob API: Execution Role Permissions
CreateDomain API: Execution Role Permissions
CreateImage and UpdateImage APIs: Execution Role Permissions
CreateNotebookInstance API: Execution Role Permissions
CreateHyperParameterTuningJob API: Execution Role Permissions
CreateProcessingJob API: Execution Role Permissions
CreateTrainingJob API: Execution Role Permissions
CreateModel API: Execution Role Permissions
AWS Managed Policies for Amazon SageMaker
AmazonSageMakerFullAccess
AmazonSageMakerReadOnly
SageMaker Updates to AWS Managed Policies
Amazon SageMaker API Permissions: Actions, Permissions, and Resources Reference
Troubleshooting Amazon SageMaker Identity and Access
I Am Not Authorized to Perform an Action in SageMaker
I Am Not Authorized to Perform iam:PassRole
I Want to View My Access Keys
I'm an Administrator and Want to Allow Others to Access SageMaker
I Want to Allow People Outside of My AWS Account to Access My SageMaker Resources
Logging and Monitoring
Compliance Validation for Amazon SageMaker
Resilience in Amazon SageMaker
Infrastructure Security in Amazon SageMaker
SageMaker Scans AWS Marketplace Training and Inference Containers for Security Vulnerabilities
Connect to Resources in a VPC
Connect SageMaker Studio Notebooks to Resources in a VPC
Default communication with the internet
VPC only communication with the internet
Requirements to use VPC only mode
Connect a Notebook Instance to Resources in a VPC
Default communication with the internet
VPC communication with the internet
Security and Shared Notebook Instances
Run Training and Inference Containers in Internet-Free Mode
Network Isolation
Network isolation with a VPC
Connect to SageMaker Through a VPC Interface Endpoint
Create a VPC Endpoint Policy for SageMaker
Create a VPC Endpoint Policy for Amazon SageMaker Feature Store
Connect to SageMaker Studio Through an Interface VPC Endpoint
Create a VPC Endpoint Policy for SageMaker Studio
Allow Access Only from Within Your VPC
Connect to a Notebook Instance Through a VPC Interface Endpoint
Connect Your Private Network to Your VPC
Create a VPC Endpoint Policy for SageMaker Notebook Instances
Restrict Access to Connections from Within Your VPC
Connect Your Private Network to Your VPC
Give SageMaker Access to Resources in your Amazon VPC
Give SageMaker Processing Jobs Access to Resources in Your Amazon VPC
Configure a Processing Job for Amazon VPC Access
Configure Your Private VPC for SageMaker Processing
Ensure That Subnets Have Enough IP Addresses
Create an Amazon S3 VPC Endpoint
Use a Custom Endpoint Policy to Restrict Access to S3
Restrict Package Installation on the Processing Container
Configure Route Tables
Configure the VPC Security Group
Connect to Resources Outside Your VPC
Give SageMaker Training Jobs Access to Resources in Your Amazon VPC
Configure a Training Job for Amazon VPC Access
Configure Your Private VPC for SageMaker Training
Ensure That Subnets Have Enough IP Addresses
Create an Amazon S3 VPC Endpoint
Use a Custom Endpoint Policy to Restrict Access to S3
Restrict Package Installation on the Training Container
Configure Route Tables
Configure the VPC Security Group
Connect to Resources Outside Your VPC
Give SageMaker Hosted Endpoints Access to Resources in Your Amazon VPC
Configure a Model for Amazon VPC Access
Configure Your Private VPC for SageMaker Hosting
Ensure That Subnets Have Enough IP Addresses
Create an Amazon S3 VPC Endpoint
Use a Custom Endpoint Policy to Restrict Access to Amazon S3
Restrict Package Installation on the Model Container with a Custom Endpoint Policy
Add Permissions for Endpoint Access for Containers Running in a VPC to Custom IAM Policies
Configure Route Tables
Connect to Resources Outside Your VPC
Give Batch Transform Jobs Access to Resources in Your Amazon VPC
Configure a Batch Transform Job for Amazon VPC Access
Configure Your Private VPC for SageMaker Batch Transform
Ensure That Subnets Have Enough IP Addresses
Create an Amazon S3 VPC Endpoint
Use a Custom Endpoint Policy to Restrict Access to S3
Restrict Package Installation on the Model Container
Configure Route Tables
Configure the VPC Security Group
Connect to Resources Outside Your VPC
Give Amazon SageMaker Clarify Jobs Access to Resources in Your Amazon VPC
Configure a SageMaker Clarify Job for Amazon VPC Access
SageMaker Clarify Job Amazon VPC Subnets and Security Groups
Configure a Model Amazon VPC for Inference
Configure Your Private Amazon VPC for SageMaker Clarify jobs
Connect to Resources Outside Your Amazon VPC
Configure the Amazon VPC Security Group
Give SageMaker Compilation Jobs Access to Resources in Your Amazon VPC
Configure a Compilation Job for Amazon VPC Access
Configure Your Private VPC for SageMaker Compilation
Ensure That Subnets Have Enough IP Addresses
Create an Amazon S3 VPC Endpoint
Use a Custom Endpoint Policy to Restrict Access to S3
Add Permissions for Compilation Job Running in a Amazon VPC to Custom IAM Policies
Configure Route Tables
Configure the VPC Security Group
Monitor Amazon SageMaker
Monitor Amazon SageMaker with Amazon CloudWatch
SageMaker Endpoint Invocation Metrics
SageMaker Multi-Model Endpoint Metrics
SageMaker Jobs and Endpoint Metrics
SageMaker Ground Truth Metrics
SageMaker Feature Store Metrics
SageMaker Pipelines Metrics
Log Amazon SageMaker Events with Amazon CloudWatch
Log Amazon SageMaker API Calls with AWS CloudTrail
SageMaker Information in CloudTrail
Operations Performed by Automatic Model Tuning
Understanding SageMaker Log File Entries
Automating Amazon SageMaker with Amazon EventBridge
Training job state change
Hyperparameter tuning job state change
Transform job state change
Endpoint state change
Feature group state change
Model package state change
Pipeline execution state change
Pipeline step state change
SageMaker image state change
SageMaker image version state change
Availability Zones
API Reference Guide for Amazon SageMaker
Overview
Programming Model for Amazon SageMaker
Document History for Amazon SageMaker
AWS glossary

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