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LLMs in Focus: From One-Size Fits All to Verticalized Solutions

Posted Dec 19, 2023 | Views 379
# LLMs
# Verticalized Solutions
# Numbers Station
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Speakers

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Laurel Orr
AI Staff Software Engineer @ Stacklok

Laurel Orr is an AI Staff Software Engineer at Stacklok working applying generative AI to data tasks. She graduated with a PhD in Databases and Data Management from Paul G Allen School for Computer Science and Engineering at the University of Washington and then was a PostDoc at Stanford working for Chris Ré in the HazyReserach Labs. Her research interests are broadly at the intersection of artifical intelligence, foundation models, and data management. She focuses on how to train, customize, and deploy foundation models to data tasks such as data cleaning, record matching, and generating code snippets for determinisitic data transformations. This includes problems around data curation for training, efficient model training and inference for batch workloads, and prompting paradigms for high performant, personalized models.

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Venky Ganti
SVP, Product & Engineering @ Numbers Station

Venky brings over two decades of experience in software engineering and technical leadership to Numbers Station as SVP of Product & Engineering. Most recently, he served as General Manager leading several initiatives on query understanding and commerce in the ads product area at Google. Before that, he was CEO and co-founder of Mesh Dynamics, the API test automation company, which was acquired by Google in 2021. Prior to Mesh Dynamics, Venky was CTO and co-founder of Alation, the enterprise data catalog company, where he led technology and helped create the new data catalog product category.

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Demetrios Brinkmann
Chief Happiness Engineer @ MLOps Community

At the moment Demetrios is immersing himself in Machine Learning by interviewing experts from around the world in the weekly MLOps.community meetups. Demetrios is constantly learning and engaging in new activities to get uncomfortable and learn from his mistakes. He tries to bring creativity into every aspect of his life, whether that be analyzing the best paths forward, overcoming obstacles, or building lego houses with his daughter.

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SUMMARY

Dive into the realm of large language models (LLMs) as we explore the merits and limitations of 'one-size fits all' LLMs, and their role in data analytics. Through customer stories, we showcase real-world applications and contrast general LLMs with verticalized, enterprise-centric models. We address the significance of ownership structures, with a focus on open-source vs proprietary impacts on transparency and trustworthiness. Delving into the NSQL foundation models, we emphasize the importance of diverse, quality training data, especially with enterprise challenges. Lastly, we speculate on the future of LLMs, highlighting hosting solutions and the evolution towards specialized challenges.

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