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

Visual data preparation with Data Wrangler

Let's start small with our 1-month dataset. Working with a small dataset is a good way to get familiar with the data before diving into more scalable techniques. SageMaker Data Wrangler gives us an easy way to construct a data flow, a series of data preparation steps powered by a visual interface.

In the rest of this section, we'll use Data Wrangler to inspect and transform data, and then export the Data Wrangler steps into a reusable flow.

Data inspection

Let's get started with Data Wrangler for data inspection, where we look at the properties of our data and determine how to prepare it for model training. Begin by adding a new flow in SageMaker Studio; go to the File menu, then New, then Flow. After the flow starts up and connects to Data Wrangler, we need to import our data. The following screenshot shows the data import step in Data Wrangler:

Figure 4.1 – Import data source in Data...