The MLOps pipeline automates the machine learning lifecycle, from model training to deployment, which involves defining the pipeline using Python code, monitoring for dataset or model parameter changes, training new models, evaluating them, and deploying successful models to production.  Model registries like MLFlow act as version control systems for ML models, allowing for easy tracking and management. Model-serving platforms like Seldon Core provide a robust way to deploy and serve models in production, eliminating the need for custom web applications and simplifying the process for ML engineers.

Source: GBHackers