A nice view on practical data science and machine learning.Great reading for newbies, some interesting views for seasoned practitioners.
Field-tested tips, tricks, and design patterns for building Machine Learning projects that are deployable, maintainable, and secure from concept to production.
In Machine Learning Engineering in Action
, you will learn:
- 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
- Automating your troubleshooting and logging practices
Databricks solutions architect Ben Wilson lays out an approach to building deployable, maintainable production machine learning systems. You’ll adopt software development standards that deliver better code management, and make it easier to test, scale, and even reuse your machine learning code!
about the technology
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.
about the book
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.
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.
about the reader
For data scientists familiar with supervised machine learning and the basics of object-oriented programming.
about the author
has worked as a professional data scientist for more than ten years. He currently works as a resident solutions architect at Databricks, where he focuses on machine learning production architecture with companies ranging from 5-person startups to global Fortune 100. 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.