This chapter covers
- Understanding machine learning (ML) systems in production
- The complete ML life cycle from experimentation to deployment
- Essential skills for production-grade ML engineering
- Building your first ML platform
- Real-world ML project architectures
Are you ready to build production-grade machine learning (ML) systems with confidence? This book will transform you into a confident ML engineer—someone who can successfully shepherd ML projects from conception to production. Through hands-on examples and real-world scenarios, you’ll learn the following:
- How to design and implement reliable ML systems that work in production
- The complete ML life cycle, from problem formulation to monitoring
- Essential patterns for building robust ML pipelines and services
- Practical MLOps skills that companies actually need
- Real-world techniques for maintaining ML systems at scale
Whether you’re a data scientist looking to deploy models confidently, a software engineer transitioning to ML, or an ML engineer wanting to level up your production skills, this book provides the practical knowledge you need to succeed with real-world ML systems. Rather than overwhelming you with theory, we’ll take a practical approach.
Each chapter builds on the previous one, introducing new concepts and tools as we need them. Our journey through this book follows a clear progression, accomplishing the following: