We talked about:
- Simon's background
- What MLOps is and what it isn't
- Skills needed to build an ML platform that serves 100s of models
- Ranking the importance of skills
- The point where you should think about building an ML platform
- The importance of processes in ML platforms
- Weighing your options with SaaS platforms
- The exploratory setup, experiment tracking, and model registry
- What comes after deployment?
- Stitching tools together to create an ML platform
- Keeping data governance in mind when building a platform
- What comes first – the model or the platform?
- Do MLOps engineers need to have deep knowledge of how models work?
- Is API design important for MLOps?
- Simon's recommendations for furthering MLOps knowledge
Links:
- LinkedIn: https://www.linkedin.com/in/simonstiebellehner/
- Github: https://github.com/stiebels
- Medium: https://medium.com/@sistel
Free MLOps course: https://github.com/DataTalksClub/mlops-zoomcamp
Join DataTalks.Club: https://datatalks.club/slack.html
Our events: https://datatalks.club/events.html
