How to Build a Platform for Data Science Workflow

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