A model that only works in a notebook doesn't help anyone. Deployment is how you put a trained model where people and systems can actually use it, and MLOps (machine learning operations) is the set of practices that keeps it working once it's there.
You'll learn why production is different from experimentation, and how to build reproducible pipelines, track experiments and model versions with MLflow, and serve a model through an API packaged with Docker. You'll also see why deployed models go stale, and why monitoring and retraining are part of the job.
This is the final module of the Data Science course. Put everything together in the Projects section that follows.
A concise introduction to what MLOps is and why it matters: machine learning in production, models going stale after deployment, model-centric vs data-centric thinking, and a demo of a reproducible pipeline.
A hands-on course that builds a customer satisfaction project end to end: pipeline steps, data cleaning, model development and evaluation, experiment tracking, deployment, and a Streamlit app.
How to move beyond notebooks with MLflow: tracking experiments, parameters, and metrics so every model pushed to production is auditable and reproducible.
A full production-style pipeline: data collection, versioning with DVC, tracking with MLflow, serving the model with Flask and Docker, and automating deployment with CI/CD.