Book Image

Machine Learning Engineering with Python

By : Andrew P. McMahon
Book Image

Machine Learning Engineering with Python

By: Andrew P. McMahon

Overview of this book

Machine learning engineering is a thriving discipline at the interface of software development and machine learning. This book will help developers working with machine learning and Python to put their knowledge to work and create high-quality machine learning products and services. Machine Learning Engineering with Python takes a hands-on approach to help you get to grips with essential technical concepts, implementation patterns, and development methodologies to have you up and running in no time. You'll begin by understanding key steps of the machine learning development life cycle before moving on to practical illustrations and getting to grips with building and deploying robust machine learning solutions. As you advance, you'll explore how to create your own toolsets for training and deployment across all your projects in a consistent way. The book will also help you get hands-on with deployment architectures and discover methods for scaling up your solutions while building a solid understanding of how to use cloud-based tools effectively. Finally, you'll work through examples to help you solve typical business problems. By the end of this book, you'll be able to build end-to-end machine learning services using a variety of techniques and design your own processes for consistently performant machine learning engineering.

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Table of Contents (13 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Share Your Thoughts
1
Section 1: What Is ML Engineering?
Section 1: What Is ML Engineering?
Free Chapter
2
Chapter 1: Introduction to ML Engineering
Chapter 1: Introduction to ML Engineering
Technical requirements
Defining a taxonomy of data disciplines
Assembling your team
ML engineering in the real world
What does an ML solution look like?
High-level ML system design
Summary
3
Chapter 2: The Machine Learning Development Process
Chapter 2: The Machine Learning Development Process
Technical requirements
Setting up our tools
Concept to solution in four steps
Summary
4
Section 2: ML Development and Deployment
Section 2: ML Development and Deployment
5
Chapter 3: From Model to Model Factory
Chapter 3: From Model to Model Factory
Technical requirements
Defining the model factory
Designing your training system
Retraining required
Learning about learning
Persisting your models
Building the model factory with pipelines
Summary
6
Chapter 4: Packaging Up
Chapter 4: Packaging Up
Technical requirements
Writing good Python
Choosing a style
Packaging your code
Building your package
Testing, logging, and error handling
Not reinventing the wheel
Summary
7
Chapter 5: Deployment Patterns and Tools
Chapter 5: Deployment Patterns and Tools
Technical requirements
Architecting systems
Exploring the unreasonable effectiveness of patterns
Containerizing
Hosting your own microservice on AWS
Pipelining 2.0
Summary
8
Chapter 6: Scaling Up
Chapter 6: Scaling Up
Technical requirements
Scaling with Spark
Spinning up serverless infrastructure
Containerizing at scale with Kubernetes
Summary
9
Section 3: End-to-End Examples
Section 3: End-to-End Examples
10
Chapter 7: Building an Example ML Microservice
Chapter 7: Building an Example ML Microservice
Technical requirements
Understanding the forecasting problem
Designing our forecasting service
Selecting the tools
Executing the build
Summary
11
Chapter 8: Building an Extract Transform Machine Learning Use Case
Chapter 8: Building an Extract Transform Machine Learning Use Case
Technical requirements
Understanding the batch processing problem
Designing an ETML solution
Selecting the tools
Executing the build
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12
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To get the most out of this book

To get the most out of the examples in the book, you will need access to a computer or server where you have privileges to install and run Python and Apache Spark applications. For many of the examples, you will also require access to a terminal, such as Bash. The examples in the book were built on a Linux machine running Bash so you may need to translate some pieces for your operating system and terminal. For some examples using AWS, you will require an account where you can enable billing. Examples in the book used Apache Spark v3.0.2.

In Chapter 5, Deployment Patterns and Tools, we use the Managed Workflows with Apache Spark (MWAA) service from AWS. There is no free tier option for MWAA so as soon as you spin up the example, you will be charged for the environment and any instances. Ensure you are happy to do this before proceeding and I recommend closing down your MWAA instances when finished.

In Chapter 7, Building an Example ML Microservice, we build out a use case leveraging the AWS Forecast service, which is only available in a subset of AWS Regions. To check the availability in your Region, and what Regions you can switch to for that example, you can use https://aws.amazon.com/about-aws/global-infrastructure/regional-product-services/.

Technical requirements are given in most of the chapters, but to support this, there are Conda environment .yml files provided in the book repository: https://github.com/PacktPublishing/Machine-Learning-Engineering-with-Python.

If you are using the digital version of this book, we advise you to type the code yourself or access the code from the book's GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.

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