MLOps.community
A podcast by Demetrios
Categories:
401 Episodes
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The Variational Book // Yuri Plotkin // #253
Published: 13/08/2024 -
Vision and Strategies for Attracting & Driving AI Talents in High Growth // Panel // AIQCON
Published: 09/08/2024 -
Red Teaming LLMs // Ron Heichman // #252
Published: 06/08/2024 -
Balancing Speed and Safety // Panel // AIQCON
Published: 02/08/2024 -
Reliable LLM Products, Fueled by Feedback // Chinar Movsisyan // #251
Published: 30/07/2024 -
A Blueprint for Scalable & Reliable Enterprise AI/ML Systems // Panel // AIQCON
Published: 26/07/2024 -
AI Operations Without Fundamental Engineering Discipline // Nikhil Suresh // #250
Published: 23/07/2024 -
AI in Healthcare // Eric Landry // #249
Published: 19/07/2024 -
Evaluating the Effectiveness of Large Language Models: Challenges and Insights // Aniket Singh // #248
Published: 16/07/2024 -
Extending AI: From Industry to Innovation // Sophia Rowland & David Weik // #247
Published: 12/07/2024 -
Detecting Harmful Content at Scale // Matar Haller // #246
Published: 09/07/2024 -
All Data Scientists Should Learn Software Engineering Principles // Catherine Nelson // #245
Published: 05/07/2024 -
Meta GenAI Infra Blog Review // Special MLOps Podcast
Published: 03/07/2024 -
AI Agents for Consumers // Shaun Wei // #244
Published: 28/06/2024 -
ML and AI as Distinct Control Systems in Heavy Industrial Settings // Richard Howes // #243
Published: 25/06/2024 -
Accelerating Multimodal AI // Ethan Rosenthal // #242
Published: 21/06/2024 -
Navigating the AI Frontier: The Power of Synthetic Data and Agent Evaluations in LLM Development // Boris Selitser // #241
Published: 18/06/2024 -
How to Build Production-Ready AI Models for Manufacturing // [Exclusive] LatticeFlow Roundtable
Published: 14/06/2024 -
From Robotics to Recommender Systems // Miguel Fierro // #240
Published: 11/06/2024 -
Uber's Michelangelo: Strategic AI Overhaul and Impact // #239
Published: 07/06/2024
Weekly talks and fireside chats about everything that has to do with the new space emerging around DevOps for Machine Learning aka MLOps aka Machine Learning Operations.
