talk-data.com

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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Mastering skill mixes for effective ML platform teams - Simon Stiebellehner

About this event

Outline:

• MLOps teams and ML platforms
• Developing ML systems
• Building a platform team
• ML engineer vs MLOps engieer

About the speaker:

Simon Stiebellehner has been building ML Platforms for over half a decade. Currently, he is Lead MLOps Engineer at Transaction Monitoring Netherlands (TMNL), a worldwide unique initiative of the big 5 banks of the Netherlands that tackles advanced analytics for Anti Money Laundering in an unprecedented way. Next to his work at TMNL, Simon is also university lecturer for Data Mining & Data Warehousing.

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