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商品＃:
254555492
Machine Learning Engineering in Action, (Paperback)
商品＃:
254555492
TWD 2754
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產品詳情
- 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 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 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 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 inside Scoping 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 practices About 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. Table of ContentsPART 1 AN INTRODUCTION TO MACHINE LEARNING ENGINEERING1 What is a machine learning engineer?2 Your data science c
| Publication date | April, 2022 |
| Author | Ben Wilson |
| Book format | Paperback |
| Genre | Computing & Internet |
| Pages | 576 |
| Age group | Adult |
| Series title | No Series |
| Fiction/nonfiction | Non-Fiction |
| Publisher | Manning Publications |
| Reading level | Professional & Vocational |
| Subgenre | Data Science |
| Original languages | English |
| Edu focus | Engineering |
| Digital audio file format | PDF, Kindle, ePub |
| Digital file format | PDF, Kindle, ePub |
| Recording time | 0 min |
| Is collectible | N |
| Bisac subject heading | Computers |
| Edition | 1 |
| Digital reader format | PDF, Kindle, ePub |
| Language | English |
| Pub date | 20220101 |
| Item Weight | 1.5 lbs (680 grams) |
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Machine Learning Engineering in Action, (Paperback)
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Ben Wilson All Books Editorial Review
Machine Learning Engineering in Action, a paperback book published by Manning Publications, serves as an essential resource for professionals in computing and internet fields. With 576 pages of insightful content, this non-fiction book explores the intricacies of data science and machine learning engineering, making it an ideal read for those seeking to deepen their knowledge in this area. The publication is designed for a professional and vocational reading level, providing readers with practical insights and methodologies relevant in today's tech landscape.
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優點
- Comprehensive coverage of machine learning engineering
- Ideal for professional and vocational readers
- Well-structured content for easy understanding
- Includes diverse topics within data science
- Published by a reputable publisher
缺點
- No collectible edition available
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TWD 2754
立即訂購並活動它 Sunday, 十一月 01
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特色和優勢
- Learn field-tested techniques for successful machine learning projects.
- Understand data evaluation and project scoping for effective solutions.
- Implement Agile methodologies for faster prototyping and stakeholder communication.
- Adopt software engineering standards for better code management and testing.
- Receive practical advice from industry expert Ben Wilson for deploying resilient ML systems.
- Access friendly, peer-to-peer explanations illustrated with production-ready source code.
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