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

Using Elastic Inference for deep learning models

If you examine the overall cost of ML, you may be surprised to see that the bulk of your monthly cost comes from real-time inference endpoints. Training jobs, while potentially resource-intensive, run for some time and then terminate. Managed notebook instances can be shut down during off hours. But inference endpoints run 24 hours a day, 7 days a week. If you are using a deep learning model, inference endpoint costs become more pronounced, as instances with dedicated GPU capacity are more expensive than other comparable instances.

When you obtain inferences from a deep learning model, you do not need as much GPU capacity as you need during training. Elastic Inference lets you attach fractional GPU capacity to regular EC2 instances or Elastic Container Service (ECS) containers. As a result, you can get deep learning inferences quickly at a reduced cost.

The Elastic Inference section in the notebook shows how to attach an Elastic...