Python Power: How Daft Embeds Models and Revolutionizes Data Processing - Podcast 165

Dive into a 52-minute podcast featuring Sammy Sidhu, CEO and Co-Founder of Eventual, as he discusses his journey in the autonomous vehicle industry and the development of Daft, an open-source query engine for multimodal data. Explore Sammy's expertise in optimizing machine learning models, his experiences with startup acquisitions by Tesla and Toyota, and the challenges of processing unstructured and complex data. Learn about the innovative features of Daft, including governance, schema evolution, and time travel, and how it aims to revolutionize data storage and processing. Gain insights into the future of MLOps, machine learning, and data storage, particularly in relation to unstructured data retrieval and processing. Discover how Daft fits into the world of Large Language Models (LLMs) and its potential impact on various industries.

[] Sammy's preferred coffee
[] Takeaways
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[] California Raisins
[] Getting into complex data
[] Autopilot
[] Autopilot challenges
[] Shrinking Mega models
[] Architecting ML on tinyML
[] Cutting through regulation
[] Creation of Daft
[] Daft dealing with complex data
[] Easy query
[] Eventual
[] Fitting into the LLMs world
[] Chain of Thought for LLMs
[] Providing a SQL engine for LLMS today
[] Daft's take off prior to LLMS
[] Querying MLOps community podcasts
[] Takeaways from tinyML
[] Haystack type queries
[] Generative AI safety nets
[] Tying in with Daft
[] Tabular data vs unstructured or complex data needs and trade-offs
[] Toolkit for System Building
[] Tiny ML with Daft optimization
[] Out-of-the-box capability
[] Workaround
[] Open source big assumption
[] Open source 2 years behind
[] Sammy's success
[] Wrap up