Designing Machine Learning Systems

Holistic Approach to ML System Design

• 🎯 This book advocates a holistic approach to designing ML systems, considering various components and the objectives of different stakeholders.
• ✅ The goal is to create systems that are reliable, scalable, maintainable, and adaptive to changing environments and business needs.
• 📚 Content is illustrated with actual case studies, backed by ample references, and reviewed by ML practitioners and experts in specific topics like batch processing or responsible AI.

Target Audience and Scope

• 💡 This book is for anyone looking to leverage ML to solve real-world problems, encompassing both deep learning and classical algorithms.
• 🚀 It primarily focuses on ML systems at scale, relevant for medium to large enterprises and fast-growing startups, as smaller systems may benefit less from its comprehensive approach.
• 🧑‍💻 The language is specifically geared toward engineers, including ML engineers, data scientists, data engineers, ML platform engineers, and engineering managers.

Addressing Common ML Challenges

• 📊 Learn to engineer data and choose the right metrics to effectively solve business problems.
• ⚙️ Discover how to deploy models after offline experiments and establish methods to quickly detect, debug, and address issues in production.
• 🛠️ Automate and improve the often manual, slow, and error-prone process of developing, evaluating, deploying, and updating models.
• 🏗️ Lay down the foundation for shared ML platform components, such as model stores, feature stores, and monitoring tools, to be reused across various use cases.