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

Selecting an appropriate deployment strategy

As you have seen so far, the initial deployment of a machine model is only one step of making it available to consumers. New versions of models are built regularly. Before making the new models available to the consumers, the model quality and infrastructure that's needed to host the model should be evaluated carefully. There are multiple factors to consider when selecting the deployment strategy to initially deploy and continue to update models. For example, not all models can be tested in production due to budget and resource constraints. Similarly, some model consumers can tolerate the model being unavailable for certain periods.

This section will summarize the deployment strategies you can use to deploy and update real-time SageMaker Endpoints. You will get an idea of the pros and cons for each strategy, in addition to when should it be used.

Selecting a standard deployment

Model consumers are not business or revenue critical...