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MLOps Weekly Podcast

Latest episodes

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Sep 6, 2022 • 43min

MLOps Week 11: The Evolution of DevOps and the Birth of MLOps with Sam Ramji

This week on the MLOps Weekly Podcast, Simba sat down with Silicon Valley veteran and Chief Strategy Officer at DataStax, Sam Ramji to discuss the origins and success of DevOps, data computation vs cognition, & the importance of community-driven product development in MLOps.
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Aug 30, 2022 • 32min

MLOps Week 10: Vector Embeddings and AI-First Databases with Bob van Lujit

This week on the MLOps weekly podcast, Simba spoke with Bob van Lujit, CEO and Co-Founder of SeMi Technologies, creators of the open-source vector database, Weaviate. They discuss the power of embeddings in machine learning and the infrastructure needed to utilize them properly in production.Bob's Bio: Bob is the CEO and co-founder of SeMI Technologies, the business created around the open-source vector search engine Weaviate. He is a frequent speaker on open-source, digital technology, software business, and business philosophy. In addition to a TEDx talk, Bob has spoken worldwide at 100s of events on open source, business strategy, and start-ups.
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Aug 23, 2022 • 35min

MLOps Week 9: LinkedIn's Feature Store & MLOps Workflows with David Stein

Back from summer break with installment 9 of the MLOps Weekly Podcast! This week, Simba met with Senior Staff Software Engineer at LinkedIn's Machine Learning Infrastructure team, David Stein, to discuss the technical decisions and insights that led to LinkedIn's Feature Store, Feathr, and the importance of thoughtful API design when building MLOps workflows.
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Aug 2, 2022 • 34min

MLOps Week 8: The MLOps Mindset with Stefan Krawczyk

For the 8th installment of the podcast, Simba sits down with Stefan Krawczyk, who led the Model Lifecycle team at Stitchfix and has worked on data science teams at Linkedin and Nextdoor, to discuss his thought process when designing and deploying MLOps solutions.Links for listeners: - my class: https://www.getsphere.com/ml-engineering/mastering-model-deployment-and-inference?source=Instructor-LinkInPost-080222-stefan_mlopsweekly_podcast - Hamilton: https://github.com/stitchfix/hamilton - Fresh blog that helps complement the content in the podcast - https://multithreaded.stitchfix.com/blog/2022/08/02/configuration-driven-ml-pipelines/
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Jul 26, 2022 • 23min

MLOps Week 7: Scaling AutoML with Nirman Dave

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.
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Jul 19, 2022 • 31min

MLOps Week 6: A VC Breakdown of MLOps with James Alcorn

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.
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Jul 12, 2022 • 22min

MLOps Week 5: Exploring the MLOps Ecosystem with Chaoyu Yang

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
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Jun 28, 2022 • 33min

MLOps Week 4: The Future of Data Infrastructure in MLOPs with Sam Partee

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.
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Jun 20, 2022 • 30min

MLOps Week 3: MLOps From A Practitioner Turned VC with Leigh Marie Braswell, Principal at Founders Fund

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.
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Jun 14, 2022 • 36min

MLOps Week 2: Uber's Feature Store and Data Quality with Atindriyo Sanyal, Co-founder of Galileo

Atindriyo Sanyal, Co-founder of Galileo, discusses Uber's feature store and data quality in MLOps. Topics include automation in ML model lifecycle, ideal MLOps workflow, experimentation significance, decentralized vs centralized ML infrastructure comparison, evolution of feature stores, and contrasting data quality tools approaches.

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