Machine Learning Engineering in Action by Ben Wilson PAPERBACK 9781617298714
Master the art of building deployable, maintainable, and secure machine learning projects from concept to production with "Machine Learning Engineering in Action" by Ben Wilson. This comprehensive guide provides field-tested tips, tricks, and design patterns for data scientists looking to bridge the gap between ML models and real-world application. Learn to effectively evaluate data science problems, scope projects for realistic expectations and budgets, and implement process techniques that minimize wasted effort and accelerate production. The book covers essential steps such as assessing projects with standardized prototyping and statistical validation, selecting the optimal technologies and tools, and enhancing codebase understandability, maintainability, and testability. You'll also gain insights into automating troubleshooting and logging practices, ensuring a smoother transition from your data science team to end-users. Key benefits include: * Agile methodologies for fast prototyping and stakeholder communication. * Adoption of well-established software development standards for better code management. * Production-ready source code examples for practical application. * Techniques for building stable data pipelines, efficient workflows, and maintainable models. Product Summary: A practical guide for data scientists on building production-ready machine learning systems. Covers project scoping, technology selection, code maintainability, testing, and deployment. "Machine Learning Engineering in Action" by Ben Wilson, published by Manning Publications in 2022, features 576 pages and is in paperback format.
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Details:Machine Learning Engineering in Action by Ben Wilson PAPERBACK 9781617298714
Master the art of building deployable, maintainable, and secure machine learning projects from concept to production with "Machine Learning Engineering in Action" by Ben Wilson. This comprehensive guide provides field-tested tips, tricks, and design patterns for data scientists looking to bridge the gap between ML models and real-world application. Learn to effectively evaluate data science problems, scope projects for realistic expectations and budgets, and implement process techniques that minimize wasted effort and accelerate production. The book covers essential steps such as assessing projects with standardized prototyping and statistical validation, selecting the optimal technologies and tools, and enhancing codebase understandability, maintainability, and testability. You'll also gain insights into automating troubleshooting and logging practices, ensuring a smoother transition from your data science team to end-users. Key benefits include: * Agile methodologies for fast prototyping and stakeholder communication. * Adoption of well-established software development standards for better code management. * Production-ready source code examples for practical application. * Techniques for building stable data pipelines, efficient workflows, and maintainable models. Product Summary: A practical guide for data scientists on building production-ready machine learning systems. Covers project scoping, technology selection, code maintainability, testing, and deployment. "Machine Learning Engineering in Action" by Ben Wilson, published by Manning Publications in 2022, features 576 pages and is in paperback format.
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Product Description
Master the art of building deployable, maintainable, and secure machine learning projects from concept to production with "Machine Learning Engineering in Action" by Ben Wilson. This comprehensive guide provides field-tested tips, tricks, and design patterns for data scientists looking to bridge the gap between ML models and real-world application. Learn to effectively evaluate data science problems, scope projects for realistic expectations and budgets, and implement process techniques that minimize wasted effort and accelerate production. The book covers essential steps such as assessing projects with standardized prototyping and statistical validation, selecting the optimal technologies and tools, and enhancing codebase understandability, maintainability, and testability. You'll also gain insights into automating troubleshooting and logging practices, ensuring a smoother transition from your data science team to end-users. Key benefits include: * Agile methodologies for fast prototyping and stakeholder communication. * Adoption of well-established software development standards for better code management. * Production-ready source code examples for practical application. * Techniques for building stable data pipelines, efficient workflows, and maintainable models. Product Summary: A practical guide for data scientists on building production-ready machine learning systems. Covers project scoping, technology selection, code maintainability, testing, and deployment. "Machine Learning Engineering in Action" by Ben Wilson, published by Manning Publications in 2022, features 576 pages and is in paperback format.
Product Specifications
General | |
|---|---|
format | trade paperback |
Language | english |
type | textbook |
Brand | Manning Publications |
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