Machine Learning Engineering in Action – Manning Publications (2022)

Machine Learning Engineering in Action – Manning Publications (2022)
English | eBook | Size: 26.38 MB

Field-tested tips, tricks, and design patterns for building machine learning projects that are deployable, maintainable, and secure from concept to production. Veteran industry expert Ben Wilson, Principal Resident Solutions Architect at Databricks, introduces techniques for building production machine learning systems. Learn Agile methodologies for fast prototyping, the importance of planning, and adopting software development standards for better code management. Illustrated with production-ready source code, this guide is for data scientists who know machine learning and the basics of object-oriented programming.

What You’ll Learn

Evaluating data science problems for effective solutions
Scoping machine learning projects for expectations and budget
Process techniques to minimize effort and speed up production
Assessing projects with prototyping work and statistical validation
Choosing the right technologies and tools
Making codebases understandable, maintainable, and testable
Automating troubleshooting and logging practices

About the Technology
Deliver maximum performance from your models and data with reproducible techniques for building stable data pipelines, efficient workflows, and maintainable models. Learn machine learning engineering practices for resilient, adaptable, and performant ML systems, based on decades of software engineering experience.

About the Book
Teaches core principles and practices for designing, building, and delivering successful machine learning projects. Discover software engineering techniques for resilient architectures and consistent cross-team communication, with methods used to solve real-world projects, based on the author’s extensive experience.

What’s Inside

Scoping machine learning projects
Choosing the right technologies and design
Making codebases maintainable and testable
Automating troubleshooting and logging

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