From Model to Market: MLOps Engineering: Versioning, Monitoring, and Deploying AI Models in Production

Building a model is only the beginning. The real challenge is turning that model into a reliable product that runs in production, scales with users, and continues to perform over time. "From Model to Market" is a practical, engineering focused guide to MLOps. It shows you how to take AI models from experimentation to deployment using Python and modern production workflows. This book focuses on the systems, processes, and tools required to manage machine learning at scale. Why MLOps is critical for real world AI Without ... Read More

Building a model is only the beginning. The real challenge is turning that model into a reliable product that runs in production, scales with users, and continues to perform over time. "From Model to Market" is a practical, engineering focused guide to MLOps. It shows you how to take AI models from experimentation to deployment using Python and modern production workflows. This book focuses on the systems, processes, and tools required to manage machine learning at scale. Why MLOps is critical for real world AI Without proper MLOps practices, even the best models fail in production. Common challenges include: lack of version control for models and data inconsistent training and deployment pipelines performance degradation over time difficulty monitoring model behavior unreliable deployment processes This book teaches you how to solve these problems with structured approaches. What you will learn fundamentals of MLOps and production ML systems model versioning and data management building reproducible training pipelines deployment strategies for machine learning models monitoring model performance and drift logging, observability, and alerting CI and CD for machine learning workflows scaling inference systems automation of model lifecycle management maintaining and updating models in production From experiment to production system Throughout the book, you will learn how to: structure machine learning projects for scalability track experiments and model versions deploy models as reliable services monitor and improve models after deployment handle model drift and data changes build automated pipelines for continuous improvement Each chapter focuses on real engineering practices used in production. Practical applications deploying ML models in SaaS products building recommendation systems real time inference services AI driven business applications enterprise machine learning platforms These examples reflect real world AI deployment scenarios. Who this book is for machine learning engineers data scientists backend engineers working with AI DevOps professionals entering MLOps teams deploying AI systems If you want to move beyond experimentation and build production ready AI systems, this book provides the roadmap. Version with control. Deploy with confidence. Operate AI systems at scale. Read Less