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

Organizing and tracking training jobs with SageMaker Experiments

A key challenge ML practitioners face is keeping track of the myriad ML experiments that need to be executed before a model achieves desired results. For a single ML project, it is not uncommon for data scientists to routinely train several different models looking for improved accuracy. HPT adds more training jobs to these experiments. Typically, there are many details to track for experiments such as hyperparameters, model architectures, training algorithms, custom scripts, metrics, result artifacts, and more.

In this section, we will discuss Amazon SageMaker Experiments, which allows you to organize, track, visualize, and compare ML models across all phases of the ML lifecycle, including feature engineering, model training, model tuning, and model deploying. SageMaker Experiments' capability tracks model lineage, allowing you to troubleshoot production issues and audit your models to meet compliance requirements...