Book Image

Learn Amazon SageMaker

By : Julien Simon
Book Image

Learn Amazon SageMaker

By: Julien Simon

Overview of this book

Amazon SageMaker enables you to quickly build, train, and deploy machine learning (ML) models at scale, without managing any infrastructure. It helps you focus on the ML problem at hand and deploy high-quality models by removing the heavy lifting typically involved in each step of the ML process. This book is a comprehensive guide for data scientists and ML developers who want to learn the ins and outs of Amazon SageMaker. You’ll understand how to use various modules of SageMaker as a single toolset to solve the challenges faced in ML. As you progress, you’ll cover features such as AutoML, built-in algorithms and frameworks, and the option for writing your own code and algorithms to build ML models. Later, the book will show you how to integrate Amazon SageMaker with popular deep learning libraries such as TensorFlow and PyTorch to increase the capabilities of existing models. You’ll also learn to get the models to production faster with minimum effort and at a lower cost. Finally, you’ll explore how to use Amazon SageMaker Debugger to analyze, detect, and highlight problems to understand the current model state and improve model accuracy. By the end of this Amazon book, you’ll be able to use Amazon SageMaker on the full spectrum of ML workflows, from experimentation, training, and monitoring to scaling, deployment, and automation.
Table of Contents (19 chapters)
Section 1: Introduction to Amazon SageMaker
Section 2: Building and Training Models
Section 3: Diving Deeper on Training
Section 4: Managing Models in Production

Chapter 12: Automating Machine Learning Workflows

In the previous chapter, you learned how to deploy machine learning models in different configurations, using both the SageMaker SDK and the boto3 SDK. We used their APIs in Jupyter notebooks, the preferred way to experiment and iterate quickly.

However, running notebooks for production tasks is not a good idea. Even if your code has been carefully tested, what about monitoring, logging, creating other AWS resources, handling errors, rolling back, and so on? Doing all of this right would require a lot of extra work and code, opening the possibility for more bugs. A more industrial approach is required.

In this chapter, you'll learn how to automate machine learning workflows with AWS services purposely built to bring repeatability, predictability, and robustness. Complex workflows can be triggered with a few simple APIs, saving you time, effort, and frustration. You'll see how you can preview infrastructure changes before...