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

Deploying models to container services

Previously, we saw how to fetch a model artifact in S3 and how to extract the actual model from it. Knowing this, it's pretty easy to deploy it on a container service, such as Amazon ECS, Amazon EKS, or Amazon Fargate.

Maybe it's company policy to deploy everything in containers, maybe you just like them, or maybe both! Whatever the reason is, you can definitely do it. There's nothing specific to SageMaker here, and the AWS documentation for these services will tell you everything you need to know.

A sample high-level process could look like this:

  1. Train a model on SageMaker.
  2. When training is complete, grab the artifact and extract the model.
  3. Push the model to a Git repository.
  4. Write a task definition (for ECS and Fargate) or a pod definition (for EKS). It could use one of the built-in containers or your own. Then, it could run a model server or your own code to clone the model from your Git repository...