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The "MLOps for model development and monitoring" course
A course that shows MLOps through the eyes of an engineer who builds and runs the platform. I am the program expert for every module and was responsible for the curriculum.
Numbers
Before
- Many courses teach MLOps from the data science side and around ready-made tools.
- A course that shows MLOps through the eyes of a platform engineer was missing.
- Back in my 2024 year-end post I promised to launch my own course.
After
- The course has been in the Practicum catalog since April 2026: 10 modules, the full MLOps cycle, Dev, ML and Ops tracks.
- Graduates reach the junior MLOps level.
- Hundreds of hours of work: rebuilding the program, refactoring modules, deadlines. Real product development, not a set of lectures.
What I did
Built the core of the program for today's market down to modules and lessons.
Wrote the briefs for the authors and led a team of 5+ authors, reviewed the materials.
Designed the course infrastructure together with the DevOps team and optimized cloud costs per student.
Focus on infrastructure: hands-on work with ClearML, Kubernetes, MLflow and Airflow, inference on Kubernetes and KServe, observability of models and infrastructure, data quality.
GPUs were deliberately left out: that is the next level, closer to LLMOps.
What the course teaches
ClearMLKubernetesMLflowAirflowKServeobservabilitydata quality
My role
Program expert for every module of the course: responsible for the curriculum. Working on the course since July 2025.