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