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

Chapter 3: Data Labeling with Amazon SageMaker Ground Truth

One of the biggest barriers to ML projects in most companies is access to labeled training data. At one company we worked with, we were trying to identify consumer-impacting outages. The customer had a lot of data from each layer of their application stack, but they couldn't agree on how to define an outage. Is an outage when a load balancer is down? Probably not – we have redundancy in the infrastructure layer. Is an outage when a customer can't access the service for over 10 minutes? That's probably too granular; a single customer might have problems due to local network connectivity issues. So, what exactly do we mean by an outage? How can we automatically label our training data as outage or not an outage?

In this chapter, we'll review labeling data using SageMaker Ground Truth. We'll cover common challenges associated with large datasets and potentially biased data.

The following...