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.

Related Content you might be interested in

Current Title:

Small book image
Machine Learning Engineering with Python
Andrew P. McMahon
Nov, 2021 | 276 pages
Small book image
Machine Learning Engineering with MLflow.
Natu Lauchande
Aug, 2021 | 248 pages
Small book image
Practical Machine Learning on Databricks
Debu Sinha
Nov, 2023 | 244 pages
Small book image
Automated Machine Learning on AWS
Trenton Potgieter
Apr, 2022 | 420 pages
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
Why subscribe?
12
Other Books You May Enjoy
Other Books You May Enjoy
Packt is searching for authors like you
Share Your Thoughts

Selecting the tools

Now that we have a high-level design in mind and we have written down some clear technical requirements, we can begin to select the toolset we will use to implement our solution.

One of the most important considerations on this front will be what framework we use for modeling our data and building our forecasting functionality. Given that the problem is a time series modeling problem with a need for fast retraining and prediction, we can consider the pros and cons of a few options that may fit the bill before proceeding.

The results of this exercise are shown in Figure 7.3:

Figure 7.3 – The considered pros and cons of some different ML toolkits for solving this forecasting problem

Based on the information in Figure 7.3, it looks like the Prophet library would be a good choice and offer a nice balance between predictive power, desired time series capabilities, and experience among the developers and scientists on the team.

...
Previous Section
End of Section 5
Next Section