Agentic Conversations (formally mlops.community)
Demetrios
Episodes
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Databricks Model Serving V2 // Rafael Pierre // Coffee Sessions #125
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Monitoring Unstructured Data // Aparna Dhinakaran & Jason Lopatecki // Lightning Sessions #2
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Trustworthy Machine Learning // Kush Varshney // Coffee Sessions #124
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RECOMMENDER SYSTEM: Why They Update Models 100 Times a Day // Gleb Abroskin // MLOps Coffee Sessions #123
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Scaling Similarity Learning at Digits // Hannes Hapke // Coffee Sessions #122
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Bringing DevOps Agility to ML// Luis Ceze // Coffee Sessions #121
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Feathr: LinkedIn's High-performance Feature Store // David Stein // Coffee Sessions #120
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MLOps at DoorDash // Hien Luu and DoorDash Leads // Coffee Sessions #119
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ML Platforms, Where to Start? // Olalekan Elesin // Coffee Sessions #118
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Data Engineering for ML // Chad Sanderson // Coffee Sessions #117
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Scaling Machine Learning with Data Mesh // Shawn Kyzer // Coffee Sessions #116
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How Hera is an Enabler of MLOps Integrations // Flaviu Vadan // Coffee Sessions #115
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Product Enrichment and Recommender Systems // Marc Lindner and Amr Mashlah // Coffee Sessions #114
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Building Better Data Teams // Leanne Fitzpatrick // Coffee Sessions #113
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MLX: Opinionated ML Pipelines in MLflow // Xiangrui Meng // Coffee Sessions #112
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More than a Cache: Turning Redis into a Composable, ML Data Platform // Samuel Partee // Coffee Sessions #111
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Just Fetch the Data and then... // David Bayliss // Coffee Sessions #110
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Why You Need More Than Airflow // Ketan Umare // Coffee Sessions #109
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ML Flow vs Kubeflow 2022 // Byron Allen // Coffee Sessions #108
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Why and When to Use Kubeflow for MLOps // Ryan Russon // Coffee Sessions #107
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