Chapter 3

Building ML Pipelines

Getting a model out of the notebook and into production is the second half of the ML engineer's job. This chapter covers the full MLOps lifecycle — Docker, deployment strategies, CI/CD, monitoring for data drift, scalability, cost, security, and building demos that get you hired.

• 1. From Notebook to the World→
• 2. Docker and Deployment Options→
• 3. CI/CD and Versioning Everything→
• 4. Monitoring and Data Drift→
• 5. Scalability, Cost, and Security→
• 6. Prototypes, Demos, and Your Portfolio→