Simba Khadder
MLOps Weekly Podcast
Join each week as we talk to MLOps operators, practitioners, and professionals about the current state of MLOps.
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Episodes
MLOps Week 7: Scaling AutoML with Nirman Dave 26.07.2022 23:14
For week 7 of the MLOps Weekly Podcast, Simba sat down with Nirman Dave, Co-founder and CEO at Obviously AI, to discuss the practical and operational benefits of AutoML in production.
MLOps Week 6: A VC Breakdown of MLOps with James Alcorn 19.07.2022 31:03
This week, Simba sat down with James Alcorn, Principal at Zetta Ventures, to discuss signals of success in MLOps platforms and his assessment template for MLOps founders.
MLOps Week 5: Exploring the MLOps Ecosystem with Chaoyu Yang 12.07.2022 22:28
This week, Simba sat down with Co-Founder and CEO of BentoML, Chaoyu Yang, to discuss various sub-categories of MLOps providers, the prevalence of open-source platforms, and what advanced MLOps solutions should look like. Music provided by Chroma Sea: https://open.spotify.com/artist/4UEQ4gyve4UpoAGUgV816o
MLOps Week 4: The Future of Data Infrastructure in MLOPs with Sam Partee 28.06.2022 33:13
For our fourth episode, we sat down with Sam Partee, Principal Applied AI Engineer at Redis, to discuss the evolution of data tools in MLOps and their ideal future.
MLOps Week 3: MLOps From A Practitioner Turned VC with Leigh Marie Braswell, Principal at Founders Fund 20.06.2022 29:55
In this week's episode, we sit down with Leigh Marie Braswell, Principal at Founders Fund, to discuss her experience as a PM at Scale AI, her transition from an MLOps practitioner to an investor, and her thoughts on the future of MLOps.
MLOps Week 2: Uber's Feature Store and Data Quality with Atindriyo Sanyal, Co-founder of Galileo 14.06.2022 36:23
This week, we sit down with Atindriyo Sanyal to discuss feature stores at Uber and the importance of data quality.
MLOps Week 1: Rethinking MLOps Observability with Bernease Herman 06.06.2022 37:42
This week, our host, Simba Khadder, sits down with Bernease Herman from Why Labs to discuss the importance of observability in MLOps
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