Machine Learning Engineering in Action
Build production-ready, maintainable, and secure machine learning systems with real-world engineering strategies.
 
Course Description
Machine Learning Engineering in Action is a practical, experience-driven guide that teaches you how to transform machine learning prototypes into scalable, reliable, and secure production systems. Instead of focusing on theory alone, this course emphasizes the engineering skills, design patterns, and development workflows required to deliver ML solutions that work in real business environments.
 
You’ll learn a systematic approach to evaluating data science problems, designing robust ML pipelines, selecting the right tools, and implementing practices that ensure your models remain performant and maintainable over time. The curriculum highlights Agile-inspired workflows, statistical validation techniques, and software engineering best practices that streamline development and reduce wasted effort.
 
Drawing on field-tested guidance from industry expert Ben Wilson, this course demonstrates how to optimize your codebase, improve testability, automate troubleshooting, and prepare your ML projects for seamless handoff from data science teams to production users. Each concept is presented in a practical, conversational style and reinforced with examples and production-ready code.
 
What You’ll Learn
How to evaluate data science problems to determine the most effective solution
Scoping machine learning projects around performance goals, user needs, and budget constraints
Process techniques that reduce development friction and speed up deployment
Using standardized prototyping and statistical validation to assess project viability
Selecting the right tools, platforms, and technologies for your ML stack
Writing maintainable, testable, and scalable machine learning code
Automating troubleshooting, monitoring, and logging for ML systems
Delivering ML solutions from development to production with confidence
 
This course is ideal for ML engineers, data scientists, and developers who want to take their machine learning projects beyond experimentation—and into real, dependable production environments.