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)
1
Section 1: Introduction to Amazon SageMaker
4
Section 2: Building and Training Models
11
Section 3: Diving Deeper on Training
14
Section 4: Managing Models in Production

Building a fully custom container for scikit-learn

In this example, we're going to build a fully custom container without any AWS code. We'll use it to train a scikit-learn model on the Boston Housing dataset, using a generic Estimator. With the same container, we'll deploy the model thanks to a Flask web application.

We'll proceed in a logical way, first taking care of the training, then updating the code to handle deployment.

Training with a fully custom container

Since we can't rely on Script Mode anymore, the training code needs to be modified. This is what it looks like, and you'll easily figure out what's happening here:

#!/usr/bin/env python
import pandas as pd import joblib, os, json
if __name__ == '__main__':    config_dir = '/opt/ml/input/config'    training_dir = '/opt/ml/input/data/training'    model_dir = '/opt/ml/model'
 ...