Machine Learning Engineering in Action
Transform your machine learning ideas into production-ready systems with practical, field-tested strategies. Machine Learning Engineering in Action provides the essential tools, design patterns, and best practices for building ML projects that are deployable, maintainable, and secure.
What You’ll Learn
Evaluate data science problems to identify the most effective solutions
Scope machine learning projects around real-world usage expectations and budgets
Apply efficient process techniques that reduce wasted effort and accelerate deployment
Validate and assess projects with standardized prototyping and statistical analysis
Select the right tools and technologies for your ML workflows
Write clear, maintainable, and testable codebases
Automate troubleshooting, logging, and operational workflows
Delivering an ML model from a data science lab to production can be challenging—but this book makes it achievable. Written by Ben Wilson, Principal Resident Solutions Architect at Databricks, the guide distills years of hands-on experience into actionable techniques for building robust machine learning systems.
Wilson emphasizes Agile methodologies, stakeholder collaboration, and strong software development principles to help you design scalable, testable architectures. Each concept is presented in an accessible, peer-to-peer style and supported by production-ready code samples you can adapt immediately.
About the Technology
Modern ML engineering bridges the gap between data science and production systems. By applying proven software engineering practices, you’ll learn to create reproducible, stable, and efficient ML pipelines that perform reliably at scale.
Machine Learning Engineering in Action teaches you how to design, build, and deliver machine learning projects using modular design, experimental workflows, and resilient architectures. Each technique stems from real-world industry experience and is backed by reproducible results.
What’s Inside
Scoping and planning ML projects effectively
Choosing appropriate technologies and frameworks
Building maintainable and testable ML codebases
Implementing automated monitoring, logging, and troubleshooting
About the Reader
For data scientists and ML practitioners with a solid grasp of machine learning fundamentals and object-oriented programming.