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 11: Monitoring Production Models with Amazon SageMaker Model Monitor and Clarify

Monitoring production machine learning (ML) models is a critical step to ensure that the models continue to meet business needs. Besides the infrastructure hosting the model, there are other important aspects of ML models that should be monitored regularly. As models age over a period of time, the real-world inference data distribution may change as compared to the data used for training the model. For example, consumer purchase patterns may change in the retail industry and economic conditions such as mortgage rates may change in the financial industry.

This gradual misalignment between the training and the live inference datasets can have a big impact on model predictions. Model quality metrics such as accuracy may degrade over time as well. Degraded model quality has a negative impact on business outcomes. Regulatory requirements, such as ensuring that ML models are unbiased and explainable...