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


In this final chapter, you learned different techniques that help to reduce prediction costs with SageMaker. First, you saw how to use autoscaling to scale prediction infrastructure according to incoming traffic. Then, you learned how to deploy an arbitrary number of models on the same endpoint, thanks to multi-model endpoints.

We also worked with Amazon Elastic Inference, which allows you to add fractional GPU acceleration to a CPU-based instance, and to find the right cost-performance ratio for your application. We then moved on to Amazon SageMaker Neo, an innovative capability that compiles models for a given hardware architecture, both for EC2 instances and embedded devices. Finally, we built a cost optimization checklist that will come in handy in your upcoming SageMaker projects.

You've made it to the end. Congratulations! You now know a lot about SageMaker. Now, go grab a dataset, build cool stuff, and let me know about it!