Reinforcement Fine-Tuning and the Future of Specialized AI Models
What if building a custom AI model for your business was as simple as giving feedback—no massive labeled datasets required? In this episode, we sit down with Travis Addair, CTO and Co-Founder of Predibase, creators of the first reinforcement fine-tuning platform, to explore the future of specialized AI.Discover how reinforcement fine-tuning is revolutionizing model customization, enabling you to start fast, adapt to your unique data, and keep improving through human feedback. Whether you’re an AI enthusiast or a business leader, you’ll learn how this breakthrough is making advanced AI accessible to everyone.Highlights:How reinforcement fine-tuning simplifies building custom modelsThe impact of human feedback on continuous model improvementMaking advanced AI accessible with minimal labeled data
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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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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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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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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.
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.
Netherlands