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ML Platform PodcastEpisodes
Episodes of ML Platform Podcast
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On this episode of the ML Platform Podcast, Chaoyu Yang discusses the MLOps stack's model serving, model registry and feature store components, online model training, large language model deployment, LLM agents, and more.
On this episode of the ML Platform Podcast, we have Olalekan Elesin as our guest. Olalekan shares insights on building the ML Platform at Scout24, including problems to be solved, reasons for going multi-cloud, point solutions vs. end-to-end pl
On this episode of the ML Platform Podcast, Mike Del Balso is our guest. Mike shares insights on his journey from Google to Tecton, building the ML platform (Michelangelo) at Uber, feature platforms, vector databases, and the future of the MLOp
On this special, end-of-the-year episode of the ML Platform Podcast, Piotr Niedzwiedzi and Aurimas Griciūnas discuss the state of MLOps and LLMOps, the impact of LLMs on layoffs and ML team composition, unsolved LLM challenges, use cases that d
On this episode of the ML Platform Podcast, we have Eduardo Bonet as our guest. Eduardo shares insights on the Staff Incubation Engineer’s work at GitLab, the relationship between MLOps and DevOps, the future of metadata tracking, CI/CD and ML
On this episode of the ML Platform Podcast, we have Mikiko Bazeley as our guest. Mikiko shares insights on the ML Platform at Mailchimp, including success stories and golden paths, team structures, templating, generative AI use cases, feedback
On this episode of the ML Platform Podcast, we have Stefan Krawczyk as our guest. Stefan shares insights on the ML Platform at Stitch Fix, including problems solved, success stories and golden paths, team structures and management, product mana
In this episode of MLOps Live, Stephen and Sabine speak with Leanne Fitzpatrick, Director of Data Science at Financial TImes. Leanne leads a team of data scientists and engineers who are responsible for building data-driven products and solutio
In this episode of MLOps Live, Stephen and Sabine speak with Marcin Tuszyński, Data Scientist at ReSpo.Vision, a consulting firm that uses artificial intelligence to revolutionize football analysis. They discuss how AI and MLOps have revolution
In this episode of MLOps Live, Stephen speaks with David Hershey, Vice President at Unusual Ventures, a seed-stage venture capital firm offering hands-on support and expertise to startups on their early-stage journey. David was also previously
In this episode of MLOps Live, Stephen and Sabine speak with Delina Ivanova, Director of Analytics at Mistplay, a loyalty platform for mobile gamers. Delina was also previously the Associate Director of Data and Insights at HelloFresh. This epi
In this episode of MLOps Live, Stephen and Sabine speak with Andreas Malekos, Head of Artificial Intelligence at Continuum Industries, and Ivan Chon-Hon Chan, AI Engineer at the same company. This week’s episode is centered around leveraging ML
In this episode of MLOps Live, Stephen and Sabine speak with Silas Bempong and Abhijit Ramesh, both Machine Learning Research Engineers at Theta Tech AI. This week’s episode is centered around doing MLOps for Clinical Research Studies. They del
In this episode of MLOps Live, Sabine and Stephen speak with Jason Flaks, Co-founder and CTO at Xembly. Jason gives insight into deploying conversational AI products in production environments. He shares his background in music composition, Mat
In this episode of MLOps Live, Stephen and Sabine speak with Kacper Lukawski, a Developer Advocate at Qdrant. They discuss the applications vector search can be useful in; why vector search engines can be more useful than keyword search engines
In this episode of MLOps Live, Piotr is joined by Aurimas Griciūnas, a Senior Solutions Architect at neptune.ai. Aurimas elaborates on the distinctions between machine learning (ML) platforms and data platform teams and the advantages of automa
In this episode of MLOps Live, Stephen is joined by Itay Ben Haim, a software team lead at Superwise. Itay discusses the advantages of automating an ML stack and how it can give data scientists more time to innovate and develop solutions that g
In this episode of MLOps Live, Stephen and Sabine are joined by Vishnu Rachakonda, Data Scientist at firsthand. Vishnu discusses setting up MLOps at a healthcare startup and how machine learning can help unlock the value of data to make people
In this episode of MLOps Live, Stephen is joined by Shirsha Ray C, Director of Engineering at TR Labs. Shirsha discusses standards and best practices for delivering ML solutions with DevOps and explores metrics to measure performance using AWS
In this episode of MLOps Live, Stephen is joined by Laszlo Sragner, Founder at Hypergolic. Laszlo discusses how important clean code and architecture are in developing ML products and uses case studies to explain how teams optimize ML products
In this episode of MLOps Live, Sabine and Stephen are joined by Simon Stiebellehner, a Lead MLOps Engineer at TMNL (Transaction Monitoring Netherlands). Simon explains how DevOps engineers can transition to MLOps engineers, the approaches MLOps
In this episode of MLOps Live, Sabine and Stephen are joined by Phil Basford, CTO of AI and ML at Inawisdom.com. Phil talks about what it takes to architect a good ML solution. He goes into more detail, giving use cases for AWS solutions and op
In this episode of MLOps Live, Sabine and Stephen are joined by Chaoyu Yang, Co-Founder & CEO of BentoML. They discuss model deployment setups and trade-offs for reasonable scale teams, the prevalence of open-source platforms, and how to deploy
In this episode of MLOps Live, Sabine and Stephen are joined by Duarte Carmo, Machine Learning Engineer at Amplemarket. Duarte draws on his extensive expertise in the field to offer advice on how to effectively execute MLOps within the constrai
In this episode of MLOps Live, Sabine and Stephen are joined by Adam Becker, Co-Founder at Telepath.io. Adam uses real-world examples from what he has observed teams succeed and fail at to tackle questions about AutoML and MLOps. Additionally,
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