Overview

What if you could take any machine learning project, from prototype chaos to production-ready reliability, using a pipeline you fully control and can scale with confidence? Containerized ML Pipelines is a practical, engineer-focused guide that shows you exactly how modern ML teams build, automate, and ship machine learning systems using containerization, CI/CD, Docker, and Kubernetes. Instead of theory or overcomplicated abstractions, this book gives you a clear path for turning your models into scalable, repeatable, and production-safe pipelines. You'll learn how to structure ML workflows for reliability, package and containerize models the right way, automate deployments, orchestrate training and inference workloads, optimize cost and performance in Kubernetes, and build pipelines that thrive in real-world engineering environments. Readers walk away with the skills to: - Build robust ML environments that behave the same locally, in staging, and in production - Containerize models and datasets for predictable and repeatable execution - Automate model delivery with CI/CD pipelines tailored for ML - Deploy scalable inference and training workloads on Kubernetes - Align ML workflows with engineering best practices used by top-performing teams The unique strength of this book is its direct, practical approach: you're not learning ML theory-you're learning the engineering systems, patterns, and tools that let real organizations operationalize ML at scale. Every chapter is written for working practitioners who care about clean architecture, automation, reproducibility, and real-world reliability over academic abstractions. If you're ready to build machine learning pipelines that run with speed, precision, and confidence, start reading Containerized ML Pipelines and transform the way you deliver ML systems today.

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