DataTalks.Club

From Scratch to Success: Building an MLOps Team and ML Platform - Simon Stiebellehner

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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

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