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)
1
Section 1: Processing Data at Scale
7
Section 2: Model Training Challenges
10
Section 3: Manage and Monitor Models
15
Section 4: Automate and Operationalize Machine Learning

Best practices for operationalizing ML workloads

Many organizations start their ML journey with a few experiments of building models to solve one or more business problems. Cloud platforms, in general, and ML platforms such as SageMaker make this experimentation easy by providing seamless access to elastic compute infrastructure and built-in support for various ML frameworks and algorithms. Once these experiments have proven successful, the next natural step is to move the models into production. Typically, at this time, organizations want to move out of the research-and-development phase and into operationalizing ML.

The idea of MLOps is gaining popularity these days. MLOps, at a very high level, involves bringing together people, processes, and technology to integrate ML workloads into release management, CI/CD, and operations. Without diving into all the details of MLOps, in this section, we will discuss best practices for operationalizing ML workloads using technology. We will...