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

Addressing unique labeling requirements with custom labeling workflows

Let's get started with a labeling job for our weather data. We want to label each weather report as good or bad. In order to help our workers do that, we'll make a nice frontend that shows the location of the weather station on a map and displays the reading from the weather station. We need a custom workflow because this scenario doesn't fall neatly into any of the existing Ground Truth templates.

We will have to set up the following:

  • A private workforce backed by a Cognito user pool
  • A manifest file that lists the items we want to label
  • A custom Ground Truth labeling workflow, consisting of two Lambda functions and a UI template

The notebook LabelData.ipynb in the CH02 folder of our repository walks through these steps.

A private labeling workforce

Although you can use a public workforce, most companies will want to use a private workforce to label their own data...