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.
Table of Contents (20 chapters)
Section 1: Processing Data at Scale
Section 2: Model Training Challenges
Section 3: Manage and Monitor Models
Section 4: Automate and Operationalize Machine Learning

Chapter 12: Machine Learning Automated Workflows

For machine learning (ML) models that are deployed to production environments, it's important to establish a consistent and repeatable process to retrain, deploy, and operate these models. This becomes increasingly important as you scale the number of ML models running in production. The machine learning development lifecycle (ML Lifecycle) brings with it some unique challenges in operationalizing ML workflows. This will be discussed in this chapter. We will also discuss common patterns to not only automate your ML workflows, but also implement continuous integration (CI) and continuous delivery/deployment (CD) practices for your ML pipelines.

Although we will cover various options for automating your ML workflows and building CI/CD pipelines for ML in this chapter, we will focus particularly on detailed implementation patterns using Amazon SageMaker Pipelines and Amazon SageMaker projects. SageMaker Pipelines is purpose-built...