Название: Machine Learning Engineering (MEAP) Автор: Ben I. Wilson Издательство: Manning Publications Год: 2020 Формат: PDF Страниц: 123 Размер: 10 Mb Язык: English
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, 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 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.