Meetup #99

The Role of Resource Management in MLOps

You know the old iceberg analogy, where the larger portion is hidden under the surface? Well, most of us in MLOps tend to focus on the visible, the models we need to deploy and run in production. But if we ignore resource management as our AI/ML initiatives grow, we’ll start to take on water, in the form of researchers fighting for resources, time-consuming manual workload rescheduling, and spiraling costs associated with ML inference. In this talk, the experts at run:ai show what role resource management has in MLOps, what to strive for, and how to get buy-in from IT.

Take-aways

- How resource management upgrades the role of MLOps - How to know when your org needs resource management automation - How to pitch automating resource management to budget-conscious IT folks

In this episode

Ronen Dar

Ronen Dar

CTO, Run:AI

Run:ai co-founder and CTO Ronen Dar was previously a research scientist at Bell Labs and has worked at Apple and in Intel in multiple R&D roles. As CTO, Ronen manages research and product roadmap for Run:ai, a startup he co-founded in 2018. Ronen is the co-author of many patents in the fields of storage, coding, and compression. Ronen received his B.S., M.S. and Ph.D. degrees from Tel Aviv University.

LinkedIn

Gijsbert Janssen van Doorn

Gijsbert Janssen van Doorn

Director Technical Product Marketing, Run:ai

Gijsbert Janssen van Doorn is Director of Technical Product Marketing at Run:ai. He is a passionate advocate for technology that will shape the future of how organizations run AI. Gijsbert comes from a technical engineering background, with six years in multiple roles at Zerto, a Cloud Data Management and Protection vendor.

LinkedIn

Demetrios Brinkmann

Demetrios Brinkmann

Host

Demetrios is one of the main organizers of the MLOps community and currently resides in a small town outside Frankfurt, Germany. He is an avid traveller who taught English as a second language to see the world and learn about new cultures. Demetrios fell into the Machine Learning Operations world, and since, has interviewed the leading names around MLOps, Data Science, and ML. Since diving into the nitty-gritty of Machine Learning Operations he felt a strong calling to explore the ethical issues surrounding ML. When he is not conducting interviews you can find him making stone stacking with his daughter in the woods or playing the ukulele by the campfire.

Ben Epstein

Ben Epstein

Host

Ben was the machine learning lead for Splice Machine, leading the development of their MLOps platform and Feature Store. He is now a founding software engineer at Galileo (rungalileo.io) focused on building data discovery and data quality tooling for machine learning teams. Ben also works as an adjunct professor at Washington University in St. Louis teaching concepts in cloud computing and big data analytics.