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#27 - Hagay Lupesko, VP of Engineering at MosaicML/Databricks
Manage episode 388411277 series 3385494
Content provided by Dr. Tony Hoang. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Dr. Tony Hoang or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.
Join us with Hagay Lupesko, VP of Engineering at MosaicML/Databricks, as we dive into the rapidly evolving world of large language models (LLMs). We'll discuss the latest innovations and challenges in the field, with a spotlight on MosaicML's unique contributions. Additionally, we explore how MosaicML's strategies compare with those of industry giants like OpenAI/Microsoft, Anthropic/AWS, and open-source initiatives such as Meta's LLaMA-2. Hagay provides expert insights into the varied approaches driving AI's future, making this a crucial listen for anyone interested in understanding the trends and potentials of large language models.
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Manage episode 388411277 series 3385494
Content provided by Dr. Tony Hoang. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Dr. Tony Hoang or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.
Join us with Hagay Lupesko, VP of Engineering at MosaicML/Databricks, as we dive into the rapidly evolving world of large language models (LLMs). We'll discuss the latest innovations and challenges in the field, with a spotlight on MosaicML's unique contributions. Additionally, we explore how MosaicML's strategies compare with those of industry giants like OpenAI/Microsoft, Anthropic/AWS, and open-source initiatives such as Meta's LLaMA-2. Hagay provides expert insights into the varied approaches driving AI's future, making this a crucial listen for anyone interested in understanding the trends and potentials of large language models.
159 episodes
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