While many resources teach you how to build machine learning (ML) models, few show you how to successfully deploy and maintain them in production. Machine learning operations (MLOps) remains a challenging field where most projects fail not due to model complexity, but because of the intricacies of building reliable, scalable ML systems. Mastering MLOps requires a combination of skills spanning software engineering, data science, and operations.
The first part of this book provides the practical knowledge needed to succeed with real-world ML systems. We’ll establish the foundations by exploring the complete ML life cycle, from problem formulation to monitoring, and identifying the essential skills for an ML engineer. You’ll then gain hands-on experience with the infrastructure backbone, learning to containerize applications with Docker, orchestrate them with Kubernetes, and implement essential continuous integration/continuous deployment (CI/CD) and monitoring practices that enable robust ML systems at scale.