Book Image

Automated Machine Learning on AWS

By : Trenton Potgieter
Book Image

Automated Machine Learning on AWS

By: Trenton Potgieter

Overview of this book

AWS provides a wide range of solutions to help automate a machine learning workflow with just a few lines of code. With this practical book, you'll learn how to automate a machine learning pipeline using the various AWS services. Automated Machine Learning on AWS begins with a quick overview of what the machine learning pipeline/process looks like and highlights the typical challenges that you may face when building a pipeline. Throughout the book, you'll become well versed with various AWS solutions such as Amazon SageMaker Autopilot, AutoGluon, and AWS Step Functions to automate an end-to-end ML process with the help of hands-on examples. The book will show you how to build, monitor, and execute a CI/CD pipeline for the ML process and how the various CI/CD services within AWS can be applied to a use case with the Cloud Development Kit (CDK). You'll understand what a data-centric ML process is by working with the Amazon Managed Services for Apache Airflow and then build a managed Airflow environment. You'll also cover the key success criteria for an MLSDLC implementation and the process of creating a self-mutating CI/CD pipeline using AWS CDK from the perspective of the platform engineering team. By the end of this AWS book, you'll be able to effectively automate a complete machine learning pipeline and deploy it to production.
Table of Contents (18 chapters)
1
Section 1: Fundamentals of the Automated Machine Learning Process and AutoML on AWS
5
Section 2: Automating the Machine Learning Process with Continuous Integration and Continuous Delivery (CI/CD)
8
Section 3: Optimizing a Source Code-Centric Approach to Automated Machine Learning
11
Section 4: Optimizing a Data-Centric Approach to Automated Machine Learning
14
Section 5: Automating the End-to-End Production Application on AWS

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "We define a train() function to capture the input parameters and fit an ImagePredictor() to training_data."

A block of code is set as follows:

import boto3 
import sagemaker
aws_region = sagemaker.Session().boto_session.region_name
!sm-docker build --build-arg REGION={aws_region} .

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

import sagemaker
import datetime
image_uri = "<Enter the Image URI from the sm-docker output>"
role = sagemaker.get_execution_role()
session = sagemaker.session.Session()
bucket = session.default_bucket()

Bold: Indicates a new term, an important word, or words that you see onscreen. For instance, words in menus or dialog boxes appear in bold. Here is an example: "Using the Amazon SageMaker management console, click the Open SageMaker Studio button."

Tips or important notes

Appear like this.