机器学习工程在行动（有声读物）

English | 2022 | ISBN: 9781617298714 | MP3@64 kbps | Duration: 14h 48m | 1.17 GB

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.

英文|2022 |国际标准图书编号：9781617298714 |MP3@64kbps|持续时间：14小时48分钟|1.17 GB经过现场测试的技巧、窍门和设计模式，用于构建从概念到生产都可部署、可维护和安全的机器学习项目。在《机器学习工程实践》中，您将学习评估数据科学问题以找到最有效的解决方案根据使用预期和预算确定机器学习项目的范围最大限度地减少浪费并加快生产的过程技术使用标准化的原型工作和统计验证评估项目为您的项目选择合适的技术和工具使您的代码库更易于理解、维护和测试自动化您的故障排除和日志记录实践将机器学习项目从您的数据科学团队运送给最终用户并非易事。《机器学习工程实践》将帮助您简化操作。在里面，您将找到来自经验丰富的行业专家Ben Wilson的精彩建议，他是Rancher的首席常驻解决方案架构师。Ben介绍了他用于构建可部署和可维护的生产机器学习系统的个人技术工具箱。您将了解敏捷方法对于快速原型制作和与利益相关者协商的重要性，同时对规划的重要性有新的认识。采用成熟的软件开发标准将帮助您提供更好的代码管理，并使测试、扩展甚至重用机器学习代码变得更加容易。每种方法都以友好的对等风格进行解释，并用生产就绪的源代码进行说明。

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