Amazon SageMaker Best Practices: Proven tips and tricks to build successful machine learning solutions on Amazon SageMaker

Cover
Title Page
Copyright and Credits
Dedication
Contributors
Table of Contents
Preface
Section 1: Processing Data at Scale
Chapter 1: Amazon SageMaker Overview
	Technical requirements
	Preparing, building, training and tuning, deploying, and managing ML models
	Discussion of data preparation capabilities
		SageMaker Ground Truth
		SageMaker Data Wrangler
		SageMaker Processing
		SageMaker Feature Store
		SageMaker Clarify
	Feature tour of model-building capabilities
		SageMaker Studio
		SageMaker notebook instances
		SageMaker algorithms
		BYO algorithms and scripts
	Feature tour of training and tuning capabilities
		SageMaker training jobs
		Autopilot
		HPO
		SageMaker Debugger
		SageMaker Experiments
	Feature tour of model management and deployment capabilities
		Model Monitor 
		Model endpoints
		Edge Manager
	Summary
Chapter 2: Data Science Environments
	Technical requirements
	Machine learning use case and dataset
	Creating data science environments
		Creating repeatability through IaC/CaC
		Amazon SageMaker notebook instances
		Amazon SageMaker Studio
		Providing and creating data science environments as IT services
		Creating a portfolio in AWS Service Catalog
		Amazon SageMaker notebook instances
		Amazon SageMaker Studio
	Summary
	References
Chapter 3: Data Labeling with Amazon SageMaker Ground Truth
	Technical requirements
	Challenges with labeling data at scale
	Addressing unique labeling requirements with custom labeling workflows
		A private labeling workforce
		Listing the data to label
		Creating the workflow
	Improving labeling quality using multiple workers
	Using active learning to reduce labeling time
	Security and permissions
	Summary
Chapter 4: Data Preparation at Scale Using Amazon SageMaker Data Wrangler and Processing
	Technical requirements
	Visual data preparation with Data Wrangler
	Bias detection and explainability with Data Wrangler and Clarify
	Data preparation at scale with SageMaker Processing
		Loading the dataset
		Drop columns
		Converting data types
		Scaling numeric fields
		Featurizing the date
		Simulating labels for air quality
		Encoding categorical variables
		Splitting and saving the dataset
	Summary
Chapter 5: Centralized Feature Repository with Amazon SageMaker Feature Store
	Technical requirements
	Amazon SageMaker Feature Store essentials
	Creating feature groups
	Populating feature groups
	Retrieving features from feature groups
	Creating reusable features to reduce feature inconsistencies and inference latency
	Designing solutions for near real-time ML predictions
	Summary
	References
Section 2: Model Training Challenges
Chapter 6: Training and Tuning at Scale
	Technical requirements
	ML training at scale with SageMaker distributed libraries
		Choosing between data and model parallelism
		Scaling the compute resources
		SageMaker distributed libraries
	Automated model tuning with SageMaker hyperparameter tuning
	Organizing and tracking training jobs with SageMaker Experiments
	Summary
	References
Chapter 7: Profile Training Jobs with Amazon SageMaker Debugger
	Technical requirements
	Amazon SageMaker Debugger essentials
		Configuring a training job to use SageMaker Debugger
		Analyzing the collected tensors and metrics
		Taking action
	Real-time monitoring of training jobs using built-in and custom rules
	Gaining insight into the training infrastructure and training framework
		Training a PyTorch model for weather prediction
		Analyzing and visualizing the system and framework metrics generated by the profiler
		Analyzing the profiler report generated by SageMaker Debugger
		Analyzing and implementing recommendations from the profiler report
		Comparing the two training jobs
	Summary
	Further reading
Section 3: Manage and Monitor Models
Chapter 8: Managing Models at Scale Using a Model Registry
	Technical requirements
	Using a model registry
	Choosing a model registry solution
		Amazon SageMaker model registry
		Building a custom model registry
		Utilizing a third-party or OSS model registry
	Managing models using the Amazon SageMaker model registry
		Creating a model package group
		Creating a model package
	Summary
Chapter 9: Updating Production Models Using Amazon SageMaker Endpoint Production Variants
	Technical requirements
	Basic concepts of Amazon SageMaker Endpoint Production Variants
	Deployment strategies for updating ML models with Amazon SageMaker Endpoint Production Variants
		Standard deployment
		A/B deployment
		Blue/Green deployment
		Canary deployment
		Shadow deployment
	Selecting an appropriate deployment strategy
		Selecting a standard deployment
		Selecting an A/B deployment
		Selecting a Blue/Green deployment
		Selecting a Canary deployment
		Selecting a Shadow deployment
	Summary
Chapter10: Optimizing Model Hosting and Inference Costs
	Technical requirements
	Real-time inference versus batch inference
		Batch inference
		Real-time inference
		Cost comparison
	Deploying multiple models behind a single inference endpoint
		Multiple versions of the same model
		Multiple models
	Scaling inference endpoints to meet inference traffic demands
		Setting the minimum and maximum capacity
		Choosing a scaling metric
		Setting the scaling policy
		Setting the cooldown period
	Using Elastic Inference for deep learning models
	Optimizing models with SageMaker Neo
	Summary
Chapter 11: Monitoring Production Models with Amazon SageMaker Model Monitor and Clarify
	End-to-end architectures for monitoring ML models
		Data drift monitoring
		Model quality drift monitoring
		Bias drift monitoring
		Feature attribution drift monitoring
	Technical requirements
	Basic concepts of Amazon SageMaker Model Monitor and Amazon SageMaker Clarify
	Best practices for monitoring ML models
	Summary
	References
Section 4: Automate and Operationalize Machine Learning
Chapter 12: Machine Learning Automated Workflows
	Considerations for automating your SageMaker ML workflows
		Typical ML workflows
		Considerations and guidance for building SageMaker workflows and CI/CD pipelines
		AWS-native options for automated workflow and CI/CD pipelines
	Building ML workflows with Amazon SageMaker Pipelines
		Building your SageMaker pipeline
		Data preparation step
		Model build step
		Model evaluation step
		Conditional step
		Register model step(s)
		Creating the pipeline
		Executing the pipeline
		Pipeline recommended practices
	Creating CI/CD pipelines using Amazon SageMaker Projects
		SageMaker projects recommended practices
	Summary
Chapter 13: Well-Architected Machine Learning with Amazon SageMaker
	Best practices for operationalizing ML workloads
		Ensuring reproducibility
		Tracking ML artifacts
		Automating deployment pipelines
		Monitoring production models
	Best practices for securing ML workloads
		Isolating the ML environment
		Disabling internet and root access
		Enforcing authentication and authorization
		Securing data and model artifacts
		Logging, monitoring, and auditing
		Meeting regulatory requirements
	Best practices for reliable ML workloads
		Recovering from failure
		Tracking model origin
		Automating deployment pipelines
		Handling unexpected traffic patterns
		Continuous monitoring of deployed model
		Updating model with new versions
	Best practices for building performant ML workloads
		Rightsizing ML resources
		Monitoring resource utilization
		Rightsizing hosting infrastructure
		Continuous monitoring of deployed model
	Best practices for cost-optimized ML workloads
		Optimizing data labeling costs
		Reducing experimentation costs with models from AWS Marketplace
		Using AutoML to reduce experimentation time
		Iterating locally with small datasets
		Rightsizing training infrastructure
		Optimizing hyperparameter-tuning costs
		Saving training costs with Managed Spot Training
		Using insights and recommendations from Debugger
		Saving ML infrastructure costs with SavingsPlan
		Optimizing inference costs
		Stopping or terminating resources
	Summary
Chapter 14: Managing SageMaker Features across Accounts
	Examining an overview of the AWS multi-account environment
	Understanding the benefits of using multiple AWS accounts with Amazon SageMaker
	Examining multi-account considerations with Amazon SageMaker
		Considerations for SageMaker features
	Summary
	References
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