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
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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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Retraining required

You wouldn't expect that after finishing your education, you never read a paper or book or speak to anyone again, which means you wouldn't be able to make informed decisions about what is happening in the world. So, you shouldn't expect a ML model to be trained once and then be performant forever afterward.

This idea is intuitive, but it represents a formal problem for ML models known as drift. Drift is a term that covers a variety of reasons for your model's performance dropping over time. It can be split into two main types:

  • Concept drift: This happens when there is a change in the fundamental relationship between the features of your data and the outcome you are trying to predict. Sometimes, this is also known as covariate drift. An example could be that at the time of training, you only have a subsample of data that seems to show a linear relationship between the features and your outcome. If it turns out that, after gathering...
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