Agentic Conversations (formally mlops.community)
Demetrios
Episodes
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Building a Culture of Experimentation to Speed Up Data-Driven Value // Delina Ivanova // MLOps Coffee Sessions #106
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Cleanlab: Labeled Datasets that Correct Themselves Automatically // Curtis Northcutt // MLOps Coffee Sessions #105
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MLOps + BI? // Maxime Beauchemin // MLOps Coffee Sessions #104
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Making MLFlow // Lead MLFlow Maintainer Corey Zumar // MLOps Coffee Sessions #103
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Fixing Your ML Data Blind Spots // Yash Sheth // MLOps Coffee Sessions #102
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Declarative Machine Learning Systems: Big Tech Level ML Without a Big Tech Team // Piero Molino // MLOps Coffee Sessions #101
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Scaling Real-time Machine Learning at Chime // Peeyush Agarwal // Lightning Sessions #1
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MLOps Critiques // Matthijs Brouns // MLOps Coffee Sessions #100
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CPU vs GPU // Ronen Dar & Gijsbert Janssen van Doorn // MLOps Coffee Sessions #99
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Racing the Playhead: Real-time Model Inference in a Video Streaming Environment // Brannon Dorsey // Coffee Sessions #98
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Real-Time Exactly-Once Event Processing with Apache Flink, Kafka, and Pinot //Jacob Tsafatinos // MLOps Coffee Sessions #97
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FastAPI for Machine Learning // Sebastián Ramírez // MLOps Coffee Sessions #96
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MLOps as Tool to Shape Team and Culture // Ciro Greco // MLOps Coffee Sessions #95
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Traversing the Data Maturity Spectrum: A Startup Perspective // Mark Freeman // Coffee Sessions #94
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Model Monitoring in Practice: Top Trends // Krishnaram Kenthapadi // MLOps Coffee Sessions #93
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Building the World's First Data Engineering Conference // Pete Soderling // MLOps Coffee Sessions #92
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The Shipyard: Lessons Learned While Building an ML Platform / Automating Adherence // Joseph Haaga // Coffee Sessions #91
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Bringing Audio ML Models into Production // Valerio Velardo // MLOps Coffee Sessions #90
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A Journey in Scaling AI // Gabriel Straub // MLOps Coffee Sessions #89
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ML Platform Tradeoffs and Wondering Why to Use Them // Javier Mansilla // MLOps Coffee Sessions #88
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