许多教程向您展示了如何开发从构思到部署模型的ML系统。但随着工具的不断变化，这些系统可能很快就会过时。如果没有将组件固定在一起的有意设计，这些系统将成为技术负担，容易出错，并且很快就会崩溃。
在本书中，Chip Huyen为设计快速部署、可靠、可扩展和迭代的真实世界ML系统提供了一个框架。这些系统有能力从新数据中学习，改进过去的错误，并适应不断变化的需求和环境。您将学习从项目范围界定、数据管理、模型开发、部署和基础设施到团队结构和业务分析的所有内容。
了解ML系统在生产中面临的挑战和要求
使用不同的采样和标记方法构建训练数据
利用最佳技术为您的ML模型设计功能，以避免数据泄漏
选择、开发、调试和评估最适合您的任务的ML模型
为不同的硬件部署不同类型的ML系统
探索主要的基础设施选择和硬件设计
了解ML的人性化方面，包括将ML集成到业务、用户体验和团队结构中
Designing Machine Learning Systems (Fourth Early Release)
Many tutorials show you how to develop ML systems from ideation to deployed models. But with constant changes in tooling, those systems can quickly become outdated. Without an intentional design to hold the components together, these systems will become a technical liability, prone to errors and be quick to fall apart.
In this book, Chip Huyen provides a framework for designing real-world ML systems that are quick to deploy, reliable, scalable, and iterative. These systems have the capacity to learn from new data, improve on past mistakes, and adapt to changing requirements and environments. You’ll learn everything from project scoping, data management, model development, deployment, and infrastructure to team structure and business analysis.
Learn the challenges and requirements of an ML system in production
Build training data with different sampling and labeling methods
Leverage best techniques to engineer features for your ML models to avoid data leakage
Select, develop, debug, and evaluate ML models that are best suit for your tasks
Deploy different types of ML systems for different hardware
Explore major infrastructural choices and hardware designs
Understand the human side of ML, including integrating ML into business, user experience, and team structure
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