MLOps.community
A podcast by Demetrios Brinkmann
Categorie:
383 Episodio
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RAG Has Been Oversimplified // Yujian Tang // #206
Pubblicato: 23/01/2024 -
The Myth of AI Breakthroughs // Jonathan Frankle // #205
Pubblicato: 19/01/2024 -
MLOps at the Crossroads // Patrick Barker & Farhood Etaati // #204
Pubblicato: 16/01/2024 -
Pioneering AI Models for Regional Languages // Aleksa Gordić // #203
Pubblicato: 12/01/2024 -
Small Data, Big Impact: The Story Behind DuckDB // Hannes Mühleisen & Jordan Tigani // #202
Pubblicato: 09/01/2024 -
Language, Graphs, and AI in Industry // Paco Nathan // #201
Pubblicato: 05/01/2024 -
Founding, Funding, and the Future of MLOps // Mihail Eric // #200
Pubblicato: 02/01/2024 -
Challenges Operationalizing ML (And Some Solutions) // Nathan Ryan Frank // #199
Pubblicato: 29/12/2023 -
Inferring Creativity // Nick Hasty // #198
Pubblicato: 26/12/2023 -
The Role of Infrastructure in ML // Niels Bantilan // #197
Pubblicato: 22/12/2023 -
LLMs in Focus: From One-Size Fits All to Verticalized Solutions // Venky Ganti & Laurel Orr // #196
Pubblicato: 19/12/2023 -
[Exclusive] Weights & Biases Round-table // Model Management in a Regulated Environment
Pubblicato: 15/12/2023 -
Building the Future of AI in Software Development // Varun Mohan // #195
Pubblicato: 12/12/2023 -
AI in Education Fireside Chat // LLMs in Production Conference 3
Pubblicato: 08/12/2023 -
[Exclusive] Tecton Round-table // Get your ML Application Into Production
Pubblicato: 07/12/2023 -
DSPy: Transforming Language Model Calls into Smart Pipelines // Omar Khattab // #194
Pubblicato: 05/12/2023 -
Fireside Chat with LLM Startups // LLMs in Production Conference 3
Pubblicato: 01/12/2023 -
LLMs in Biomaterials Production // Pierre Salvy // #193
Pubblicato: 28/11/2023 -
Product Engineering for LLMs // LLMs in Production Conference Part III // Panel 2
Pubblicato: 24/11/2023 -
Enterprises Using MLOps, the Changing LLM Landscape, MLOps Pipelines // Chris Van Pelt // #192
Pubblicato: 21/11/2023
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.
