Benchmarking Domain Intelligence | Data Brew | Episode 45
In this episode, Pallavi Koppol, Research Scientist at Databricks, explores the importance of domain-specific intelligence in large language models (LLMs). She discusses how enterprises need models tailored to their unique jargon, data, and tasks rather than relying solely on general benchmarks.Highlights include:- Why benchmarking LLMs for domain-specific tasks is critical for enterprise AI.- An introduction to the Databricks Intelligence Benchmarking Suite (DIBS).- Evaluating models on real-world applications like RAG, text-to-JSON, and function calling.- The evolving landscape of open-source vs. closed-source LLMs.- How industry and academia can collaborate to improve AI benchmarking.
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31:41
SWE-bench & SWE-agent | Data Brew | Episode 44
In this episode, Kilian Lieret, Research Software Engineer, and Carlos Jimenez, Computer Science PhD Candidate at Princeton University, discuss SWE-bench and SWE-agent, two groundbreaking tools for evaluating and enhancing AI in software engineering.Highlights include:- SWE-bench: A benchmark for assessing AI models on real-world coding tasks.- Addressing data leakage concerns in GitHub-sourced benchmarks.- SWE-agent: An AI-driven system for navigating and solving coding challenges.- Overcoming agent limitations, such as getting stuck in loops.- The future of AI-powered code reviews and automation in software engineering.
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36:22
Enterprise AI: Research to Product | Data Brew | Episode 43
In this episode, Dipendra Kumar, Staff Research Scientist, and Alnur Ali, Staff Software Engineer at Databricks, discuss the challenges of applying AI in enterprise environments and the tools being developed to bridge the gap between research and real-world deployment.Highlights include:- The challenges of real-world AI—messy data, security, and scalability.- Why enterprises need high-accuracy, fine-tuned models over generic AI APIs.- How QuickFix learns from user edits to improve AI-driven coding assistance.- The collaboration between research & engineering in building AI-powered tools.- The evolving role of developers in the age of generative AI.
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38:03
Multimodal AI | Data Brew | Episode 42
In this episode, Chang She, CEO and Co-founder of LanceDB, discusses the challenges of handling multimodal data and how LanceDB provides a cutting-edge solution. He shares his journey from contributing to Pandas to building a database optimized for images, video, vectors, and subtitles.Highlights include:- The limitations of traditional storage systems like Parquet for multimodal AI.- How LanceDB enables efficient querying and processing of diverse data types.- The growing importance of multimodal AI in enterprise applications.- Future trends in AI, including a shift from single models to holistic AI systems.- Predictions and "spicy takes" on AI advancements in 2025.
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42:14
Age of Agents | Data Brew | Episode 41
In this episode, Michele Catasta, President of Replit, explores how AI-driven agents are transforming software development by making coding more accessible and automating application creation.Highlights include:- The difference between AI agents and copilots in software development.- How AI is democratizing coding, enabling non-programmers to build applications.- Challenges in AI agent development, including error handling and software quality.- The growing role of AI in entrepreneurship and business automation.- Why 2025 could be the year of AI agents and what’s next for the industry.
Welcome to Data Brew by Databricks with Denny and Brooke! In this series, we explore various topics in the data and AI community and interview subject matter experts in data engineering/data science. So join us with your morning brew in hand and get ready to dive deep into data + AI! For this first season, we will be focusing on lakehouses – combining the key features of data warehouses, such as ACID transactions, with the scalability of data lakes, directly against low-cost object stores.
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