Book Image

Amazon SageMaker Best Practices

By : Sireesha Muppala, Randy DeFauw, Shelbee Eigenbrode
Book Image

Amazon SageMaker Best Practices

By: Sireesha Muppala, Randy DeFauw, Shelbee Eigenbrode

Overview of this book

Amazon SageMaker is a fully managed AWS service that provides the ability to build, train, deploy, and monitor machine learning models. The book begins with a high-level overview of Amazon SageMaker capabilities that map to the various phases of the machine learning process to help set the right foundation. You'll learn efficient tactics to address data science challenges such as processing data at scale, data preparation, connecting to big data pipelines, identifying data bias, running A/B tests, and model explainability using Amazon SageMaker. As you advance, you'll understand how you can tackle the challenge of training at scale, including how to use large data sets while saving costs, monitoring training resources to identify bottlenecks, speeding up long training jobs, and tracking multiple models trained for a common goal. Moving ahead, you'll find out how you can integrate Amazon SageMaker with other AWS to build reliable, cost-optimized, and automated machine learning applications. In addition to this, you'll build ML pipelines integrated with MLOps principles and apply best practices to build secure and performant solutions. By the end of the book, you'll confidently be able to apply Amazon SageMaker's wide range of capabilities to the full spectrum of machine learning workflows.

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Table of Contents (20 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Share your thoughts
1
Section 1: Processing Data at Scale
Section 1: Processing Data at Scale
Free Chapter
2
Chapter 1: Amazon SageMaker Overview
Chapter 1: Amazon SageMaker Overview
Technical requirements
Preparing, building, training and tuning, deploying, and managing ML models
Discussion of data preparation capabilities
Feature tour of model-building capabilities
Feature tour of training and tuning capabilities
Feature tour of model management and deployment capabilities
Summary
3
Chapter 2: Data Science Environments
Chapter 2: Data Science Environments
Technical requirements
Machine learning use case and dataset
Creating data science environment
Summary
References
4
Chapter 3: Data Labeling with Amazon SageMaker Ground Truth
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
Improving labeling quality using multiple workers
Using active learning to reduce labeling time
Security and permissions
Summary
5
Chapter 4: Data Preparation at Scale Using Amazon SageMaker Data Wrangler and Processing
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
Summary
6
Chapter 5: Centralized Feature Repository with Amazon SageMaker Feature Store
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
7
Section 2: Model Training Challenges
Section 2: Model Training Challenges
8
Chapter 6: Training and Tuning at Scale
Chapter 6: Training and Tuning at Scale
Technical requirements
ML training at scale with SageMaker distributed libraries
Automated model tuning with SageMaker hyperparameter tuning
Organizing and tracking training jobs with SageMaker Experiments
Summary
References
9
Chapter 7: Profile Training Jobs with Amazon SageMaker Debugger
Chapter 7: Profile Training Jobs with Amazon SageMaker Debugger
Technical requirements
Amazon SageMaker Debugger essentials
Real-time monitoring of training jobs using built-in and custom rules
Gaining insight into the training infrastructure and training framework
Summary
Further reading
10
Section 3: Manage and Monitor Models
Section 3: Manage and Monitor Models
11
Chapter 8: Managing Models at Scale Using a Model Registry
Chapter 8: Managing Models at Scale Using a Model Registry
Technical requirements
Using a model registry
Choosing a model registry solution
Managing models using the Amazon SageMaker model registry
Summary
12
Chapter 9: Updating Production Models Using Amazon SageMaker Endpoint Production Variants
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 SageMaker Endpoint Production Variants
Selecting an appropriate deployment strategy
Summary
13
Chapter 10: Optimizing Model Hosting and Inference Costs
Chapter 10: Optimizing Model Hosting and Inference Costs
Technical requirements
Real-time inference versus batch inference
Deploying multiple models behind a single inference endpoint
Scaling inference endpoints to meet inference traffic demands
Using Elastic Inference for deep learning models
Optimizing models with SageMaker Neo
Summary
14
Chapter 11: Monitoring Production Models with Amazon SageMaker Model Monitor and Clarify
Chapter 11: Monitoring Production Models with Amazon SageMaker Model Monitor and Clarify
Technical requirements
Basic concepts of Amazon SageMaker Model Monitor and Amazon SageMaker Clarify
End-to-end architectures for monitoring ML models
Best practices for monitoring ML models
Summary
References
15
Section 4: Automate and Operationalize Machine Learning
Section 4: Automate and Operationalize Machine Learning
16
Chapter 12: Machine Learning Automated Workflows
Chapter 12: Machine Learning Automated Workflows
Considerations for automating your SageMaker ML workflows
Building ML workflows with Amazon SageMaker Pipelines
Creating CI/CD pipelines using Amazon SageMaker Projects
Summary
17
Chapter 13:Well-Architected Machine Learning with Amazon SageMaker
Chapter 13:Well-Architected Machine Learning with Amazon SageMaker
Best practices for operationalizing ML workloads
Best practices for securing ML workloads
Best practices for reliable ML workloads
Best practices for building performant ML workloads
Best practices for cost-optimized ML workloads
Summary
18
Chapter 14: Managing SageMaker Features across Accounts
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
Summary
References
Why subscribe?
19
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Optimizing models with SageMaker Neo

In the previous section, we saw how Elastic Inference can reduce inference costs for deep learning models. Similarly, SageMaker Neo lets you improve inference performance and reduce costs by compiling trained ML models for better performance on specific platforms. While that will help in general, it's particularly effective when you are trying to run inference on low-powered edge devices.  

In order to use SageMaker Neo, you simply start a compilation job with a trained model in a supported framework. When the compilation job completes, you can deploy the artifact to a SageMaker endpoint or to an edge device using the Greengrass IoT platform.

The Model optimization with SageMaker Neo section in the notebook demonstrates how to compile our XGBoost model for use on a hosted endpoint:

  1. First, we need to get the length (number of features) of an input record:
    ncols = len(t_lines[0].split(','))
  2. Now, we'll...
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