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

Diving deep on SageMaker Autopilot

In this section, we're going to learn in detail how SageMaker Autopilot processes data and trains models. If this feels too advanced for now, you're welcome to skip this material. You can always revisit it later once you've gained more experience with the service.

First, let's look at the artifacts that SageMaker Autopilot produces.

The job artifacts

Listing our S3 bucket confirms the existence of many different artifacts:

$ aws s3 ls s3://sagemaker-us-east-2-123456789012/sagemaker/DEMO-autopilot/output/my-first-autopilot-job/

We can see many new prefixes. Let's figure out what's what:

PRE data-processor-models/PRE preprocessed-data/PRE sagemaker-automl-candidates/PRE transformed-data/PRE tuning/

The preprocessed-data/tuning_data prefix contains the training and validation splits generated from the input dataset. Each split is further broken into small CSV chunks:

  • The sagemaker-automl...