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

Understanding how SageMaker invokes your code

When we worked with built-in algorithms and frameworks, we didn't pay much attention to how SageMaker actually invoked the training and deployment code. After all, that's what "built-in" means: grab what you need off the shelf and get to work.

Of course, things are different if we want to use our own custom code and containers. We need to understand how they interface with SageMaker so that we implement them exactly right.

In this section, we'll discuss this interface in detail. Let's start with the file layout.

Understanding the file layout inside a SageMaker container

To make our life simpler, SageMaker estimators automatically copy hyperparameters and input data inside training containers. Likewise, they automatically copy the trained model from the container to S3. At deployment time, they do the reverse operation, copying the model from S3 into the container.

As you can imagine, this requires...