کتاب مهندسی یادگیری ماشین در عمل

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

Ferrying a machine learning project from your data science team to your end users is no easy task. Machine Learning Engineering in Action will help you make it simple. Inside, you’ll find fantastic advice from veteran industry expert Ben Wilson, Principal Resident Solutions Architect at Databricks.

Ben introduces his personal toolbox of techniques for building deployable and maintainable production machine learning systems. You’ll learn the importance of Agile methodologies for fast prototyping and conferring with stakeholders, while developing a new appreciation for the importance of planning. Adopting well-established software development standards will help you deliver better code management, and make it easier to test, scale, and even reuse your machine learning code. Every method is explained in a friendly, peer-to-peer style and illustrated with production-ready source code.

About the technology
Deliver maximum performance from your models and data. This collection of reproducible techniques will help you build stable data pipelines, efficient application workflows, and maintainable models every time. Based on decades of good software engineering practice, machine learning engineering ensures your ML systems are resilient, adaptable, and perform in production.