Machine Learning Engineering in Action is a roadmap to delivering successful machine learning projects. It teaches you to adopt an efficient, sustainable, and goal-driven approach that author Ben Wilson has developed over a decade of data science experience. Every method in this book has been used to solve a breakdown in a real-world project, and is illustrated with production-ready source code and easily reproducible examples. Learn more about the book here: http://mng.bz/KMqZ

1. Dependable machine
learning systems
with Machine Learning Engineering in
Action.
Take 40% off by entering slwilson4 into
the discount code box at checkout at
manning.com.

2. Following established processes
and methodology maximizes the
likelihood that your machine
learning projects will survive and
succeed for the long haul.
By adopting standard,
reproducible practices, your
projects will be maintainable over
time and easy for new team
members to understand and adapt.

3. Machine Learning Engineering in
Action is a roadmap to delivering
successful machine learning
projects. It teaches you to adopt an
efficient, sustainable, and goal-
driven approach to your projects.
You’ll learn many field-tested tips,
tricks, and design patterns for
building machine learning projects
that are deployable, maintainable,
and secure from concept to
production.

4. In this book, you’ll learn how to plan
and scope your project, manage
cross-team logistics that avoid fatal
communication failures, and design
your code’s architecture for
improved resilience. You’ll even
discover when not to use machine
learning—and the alternative
approaches that might be cheaper
and more effective. When you’re
done working through this toolbox
guide, you’ll be able to reliably
deliver cost-effective solutions for
organizations big and small alike.

5. Here’s some
of what you’ll
learn in the
book
• Evaluating data science problems to
find the most effective solution
• Scoping a machine learning project for
usage expectations and budget
• Process techniques that minimize
wasted effort and speed up production
• Assessing a project using standardized
prototyping work and statistical
validation
• Choosing the right technologies and
tools for your project
• Making your codebase more
understandable, maintainable, and
testable
• Leveraging MLFlow for automating
logging and establishing provenance

6. About the author:
Ben Wilson has worked as a
professional data scientist for more
than ten years. He currently works
as a practice lead at Databricks,
focusing on machine learning
production architecture for various
companies. Ben is the creator and
lead developer of the Databricks
Labs AutoML project, a Scala-and
Python-based toolkit that simplifies
machine learning feature
engineering, model tuning, and
pipeline-enabled modeling.

7. Take 40% off Machine Learning
Engineering in Action by entering
slwilson4 into the discount code box at
checkout at manning.com.
If you want to learn more about the
book, you can check it out on our
browser-based liveBook platform here.