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

Challenges with labeling data at scale

Besides the conceptual challenges with agreeing on how to label data, we need to consider the logistics. SageMaker Ground Truth lets you assign data labeling jobs to a human workforce. But you may face additional challenges such as the following:

  • Unique labeling logic: If our labeling case requires a custom workflow, we need to model that in Ground Truth.
  • Annotation quality: The labels applied by workers may be subject to implicit bias that affects the results.
  • Cost and time: Labeling data requires people for a period of time. If you have a very large dataset, you'll consume a lot of person-hours.
  • Security: Given that your data may be sensitive, you need to make sure that access to the data is restricted to an authorized workforce.

    Additional information

    If you need an introduction to Ground Truth, please review Chapter 2 of Learn Amazon SageMaker, written by Julien Simon.

To put these concerns into focus, let&apos...