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

Populating feature groups

After creating the feature groups, you will populate them with features. You can ingest features into a feature group using either batch ingestion or streaming ingestion, as shown in Figure 5.5:

Figure 5.5 – Ingesting features into feature groups

To ingest features into the feature store, you create a feature pipeline that can populate the feature store. A feature pipeline can include any service or capability that accepts raw data and then transforms that raw data into engineered features and puts the features in a designated feature group. Features can be ingested either in bulk in batches or streamed individually. The PutRecord API call is the core SageMaker API for ingesting features. This is used for both online and offline feature stores as well as ingesting through batch or streaming methods.

The following code block shows the usage of the PutRecord API:

##Create a record to ingest into the feature group