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 2: Handling Data Preparation Techniques

Data is the starting point of any machine learning project, and it takes lots of work to turn data into a dataset that can be used to train a model. That work typically involves annotating datasets, running bespoke scripts to preprocess them, and saving processed versions for later use. As you can guess, doing all this work manually, or building tools to automate it, is not an exciting prospect for machine learning teams.

In this chapter, you will learn about AWS services that help you build and process data. We'll first cover Amazon SageMaker Ground Truth, a capability of Amazon SageMaker that helps you quickly build accurate training datasets. Then, we'll talk about Amazon SageMaker Processing, another capability that helps you run your data processing workloads, such as feature engineering, data validation, model evaluation, and model interpretation. Finally, we'll discuss other AWS services that may help with data...