What You'll Learn

1. The core principles of MLOps and how it extends DevOps for machine learning.
2. How to design an end-to-end ML lifecycle — from data ingestion to monitoring.
3. The differences between traditional DevOps and MLOps.
4. How to build reproducible ML pipelines using modern tools like MLflow, Kubeflow, and DVC.
5. Common pitfalls, scalability strategies, and security best practices for ML in production.