This path focuses on operationalizing machine learning—building tools and infrastructure to help teams train, deploy, monitor, and maintain ML models at scale.

Who it’s for:

Learners who want to work at the intersection of data science, software engineering, and DevOps.

What you’ll learn:

• ML lifecycle and MLOps principles

• Experiment tracking with MLFlow or Weights & Biases

• Model versioning and reproducibility

• Packaging ML models using Docker

• CI/CD for ML pipelines

• Model deployment with FastAPI or Flask

• Basics of monitoring and logging in production

• Introduction to orchestration tools (Airflow, Prefect)

• Cloud deployment basics (AWS, GCP, or Azure)

What you’ll build:

• MLOps pipeline for a sample project

• Dockerized ML service with auto-updates

• Mini MLOps dashboard with metrics and version tracking