MLOps — From Notebook to Production — Cohort site
What it takes for a machine-learning model to keep working after the notebook is closed: containerization, deployment, monitoring, reproducibility, and the engineering discipline that distinguishes a prototype from a production system.
What it takes for a machine-learning model to keep working after the notebook is closed: containerization, deployment, monitoring, reproducibility, and the engineering discipline that distinguishes a prototype from a production system.
| Level | Graduate / professional |
|---|---|
| Instructor | Dr. Yaé Ulrich Gaba |
| Meeting pattern | Tuesdays + Thursdays, 14:00–16:00 lecture · Fridays 14:00–15:00 systems review (Africa/Lagos UTC+1) |
Prerequisites
Comfortable Python (functions, classes, virtual environments). At least one prior ML project (any course in the catalogue). Basic command-line proficiency. Familiarity with Git.
Grading
Five labs (50%) · two systems-design memos (20%) · final deployed capstone (30%).
Reading
Chip Huyen, Designing Machine Learning Systems (O’Reilly 2022) — recommended companion.
What this site is and isn’t
The bilingual notes (linked above) are the reference text. This cohort site is the operational layer: every week page has the lecture topic, the readings to do beforehand, the problem set or code lab, and any paper discussion. The schedule and weeks are generated from a single data file (_data/mlops.yml), so the same source drives the landing, the schedule, and every week page. If you are reading along without being in a cohort, the week pages still work as a self-study guide; the deliverables become optional, but the readings and lecture topics are the same.