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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