你的训练数据和算法本身一样，与你的数据项目的成功有关——深度学习系统中的大多数失败都与训练数据有关。但是，虽然训练数据是成功机器学习的基础，但很少有全面的资源可以帮助你在这个过程中取得成功。本实践指南解释了如何使用和扩展培训数据。你将对实现这一目标所需的概念、工具和过程有深入的了解
为生产级深度学习应用程序设计、部署和发布培训数据
与不断增长的工具生态系统集成
识别并纠正新的基于训练数据的故障模式
提高现有系统性能，避免开发风险
自信地使用自动化和加速方法来更有效地创建培训数据
通过围绕创建的数据集构建元数据，避免数据丢失
向主题专家和其他股东清楚地解释培训数据概念
成功维护、运行和改进您的系统
Training Data for Machine Learning (Fourth Early Release)
Your training data has as much to do with the success of your data project as the algorithms themselves–most failures in deep learning systems relate to training data. But while training data is the foundation for successful machine learning, there are few comprehensive resources to help you ace the process. This hands-on guide explains how to work with and scale training data. You’ll gain a solid understanding of the concepts, tools, and processes needed to
Design, deploy, and ship training data for production-grade deep learning applications
Integrate with a growing ecosystem of tools
Recognize and correct new training data-based failure modes
Improve existing system performance and avoid development risks
Confidently use automation and acceleration approaches to more effectively create training data
Avoid data loss by structuring metadata around created datasets
Clearly explain training data concepts to subject matter experts and other shareholders
Successfully maintain, operate, and improve your system
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