可靠的机器学习(影印版)

无论你是小型创业公司或跨国公司的一员，这 本实践用书为数据科学家、软件和网站可靠性工程 师、产品经理和企业主展示了如何在组织内可靠、 有效和负责地运行和建立机器学习。你将深入了解 其中涉及的方方面面，从如何在生产中进行模型监 控到如何在产品组织中运营一个完善的模型开发团 队。 通过将SRE思维应用于机器学习，作为本书作 者和工程专业人士的Cathy Chen、Kranti Parisa 、Niall、D. Sculley、Todd Underwood以及特邀 作者向你展示了如何运行高效可靠的机器学习系统 。无论你想增加收入、优化决策、解决问题，还是 理解和影响客户行为，你将学习如何执行日常的机 器学习任务，同时保持更广阔的视野。

Foreword Preface 1. Introduction The ML Lifecycle Data Collection and Analysis ML Training Pipelines Build and Validate Applications Quality and Performance Evaluation Defining and Measuring SLOs Launch Monitoring and Feedback Loops Lessons from the Loop 2. Data Management Principles Data as Liability The Data Sensitivity of ML Pipelines Phases of Data Creation Ingestion Processing Storage Management Analysis and Visualization Data Reliability Durability Consistency Version Control Performance Availability Data Integrity Security Privacy Policy and Compliance Conclusion 3. Basic Introduction to Models What Is a Model? A Basic Model Creation Work_flow Model Architecture Versus Model Definition Versus Trained Model Where Are the Vulnerabilities? Training Data Labels Training Methods Infrastructure and Pipelines Platforms Feature Generation Upgrades and Fixes A Set of Useful Questions to Ask About Any Model An Example ML System Yarn Product Click-Prediction Model Features Labels for Features