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

Using a model registry

A model registry allows you to centrally track key metadata for each model version. The granularity of metadata tracked is often dependent on the chosen implementation (Amazon SageMaker's model registry, a custom solution, or a third-party solution).  

Regardless of the implementation, the key metadata to consider includes model version identifiers, and the following information about each model version registered:

  • Model inputs: These include metadata related to the inputs and versions of those inputs used to train the model. This can include inputs such as the name of the Amazon S3 bucket storing the training data, training hyperparameters, and the Amazon Elastic Container Registry (ECR) repository or container image used for training.
  • Model performance: This includes model evaluation data such as training and validation metrics.
  • Model artifact: This includes metadata about the training model artifact. At a minimum, this includes...