num_pages: 983
description: 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 Evaluating data science problems to find the most effective solutionScoping a machine learning project for usage expectations and budgetProcess techniques that minimize wasted effort and speed up productionAssessing a project using standardized prototyping work and statistical validationChoosing the right technologies and tools for your projectMaking your codebase more understandable maintainable and testableAutomating your troubleshooting and logging practicesFerrying 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 technologyDeliver 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.About the bookMachine Learning Engineering in Action teaches you core principles and practices for designing building and delivering successful machine learning projects. You'll discover software engineering techniques like conducting experiments on your prototypes and implementing modular design that result in resilient architectures and consistent cross-team communication. Based on the author's extensive experience every method in this book has been used to solve real-world projects.What's insideScoping a machine learning project for usage expectations and budgetChoosing the right technologies for your designMaking your codebase more understandable maintainable and testableAutomating your troubleshooting and logging practicesAbout the readerFor data scientists who know machine learning and the basics of object-oriented programming.About the authorBen Wilson is Principal Resident Solutions Architect at Databricks where he developed the Databricks Labs AutoML project and is an MLflow committer.
language: English
title: Machine Learning Engineering in Action
publisher: Manning