Name: Training and Fine-Tuning LLMs to optimize AI application performance
Start: 2024-05-01T09:00:00-07:00
End: 2024-05-01T10:00:00-07:00

Building and using LLMs in an effective fashion requires an understanding of existing open-source models as well as an understanding of how LLMs are built.

Join this talk to learn more about these topics from two experts in the field. We'll have some time for Q&A at the end. See you online!

**Speaker Bios**

Kobie Crawford is the Developer Advocate for Mosaic AI at Databricks. Over his 25-year career in technology, Kobie has worked in ML Engineering, DevOps, Sales and Solutions Engineering, Education, and Customer Support -- always with a passion for making connections and facilitating conversations. After an eleven-year stint at Apple, he joined MosaicML as Head of Community, and applied this passion to open dialog among ML researchers and engineers in the generative AI space. He continues that mission in the Developer Relations team at Databricks.

Sam Raymond is a Senior Data Scientist in the Machine Learning Practice at Databricks. Sam received his PhD in Computational Engineering and Machine Learning at MIT. Prior to joining Databricks he spent several years developing courseware on digital transformation and publishing research papers in areas such as bioengineering, climate and sustainability, and simulation as a postdoctoral researcher in deep learning and data science at Stanford University and MIT.

Hosted by Rob Reed and Karen Bajza-Terlouw from your Databricks University Alliance team

Karen Bajza-Terlouw

Rob Reed

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Training and Fine-Tuning LLMs to optimize AI application performance

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Training and Fine-Tuning LLMs to optimize AI application performance

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