This is the code repository for Machine Learning Engineering with Python, published by Packt.
Manage the production life cycle of machine learning models using MLOps with practical examples
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.
This book covers the following exciting features:
- Find out what an effective ML engineering process looks like
- Uncover options for automating training and deployment and learn how to use them
- Discover how to build your own wrapper libraries for encapsulating your data science and machine learning logic and solutions
- Understand what aspects of software engineering you can bring to machine learning
- Gain insights into adapting software engineering for machine learning using appropriate cloud technologies
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
[default]
exten => s,1,Dial(Zap/1|30)
exten => s,2,Voicemail(u100)
exten => s,102,Voicemail(b100)
exten => i,1,Voicemail(s0)
Following is what you need for this book: This book is for machine learning engineers, data scientists, and software developers who want to build robust software solutions with machine learning components. If you're someone who manages or wants to understand the production life cycle of these systems, you'll find this book useful. Intermediate-level knowledge of Python is necessary.
With the following software and hardware list you can run all code files present in the book (Chapter 1-8).
| Chapter | Software required | OS required |
|---|---|---|
| 1 - 8 | Python 3, Anaconda, Docker | Windows, Mac OS X, and Linux (Any) |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
Andrew Peter (Andy) McMahon is a machine learning engineer and data scientist with experience of working in, and leading, successful analytics and software teams. His expertise centers on building production-grade ML systems that can deliver value at scale. He is currently ML Engineering Lead at NatWest Group and was previously Analytics Team Lead at Aggreko.
He has an undergraduate degree in theoretical physics from the University of Glasgow, as well as master's and Ph.D. degrees in condensed matter physics from Imperial College London. In 2019, Andy was named Data Scientist of the Year at the International Data Science Awards. He currently co-hosts the AI Right podcast, discussing hot topics in AI with other members of the Scottish tech scene.