Learning systems2026Published
MLOps Engineering 101
A practical zero-to-hero curriculum for ML engineers covering end-to-end training pipelines, infrastructure, and team-ready workflows.
Why it exists
Strong ML engineers need more than model intuition. They also need a working mental model for pipelines, infrastructure, reproducibility, and team practices that make systems usable beyond the experiment stage.
What it covers
- End-to-end training pipelines and experiment tracking.
- Containerized workflows and infrastructure setup.
- The gap between isolated notebooks and operational ML systems.