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.
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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.
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Reward Models | Data Brew | Episode 40
In this episode, Brandon Cui, Research Scientist at MosaicML and Databricks, dives into cutting-edge advancements in AI model optimization, focusing on Reward Models and Reinforcement Learning from Human Feedback (RLHF).Highlights include:- How synthetic data and RLHF enable fine-tuning models to generate preferred outcomes.- Techniques like Policy Proximal Optimization (PPO) and Direct PreferenceOptimization (DPO) for enhancing response quality.- The role of reward models in improving coding, math, reasoning, and other NLP tasks.Connect with Brandon Cui:https://www.linkedin.com/in/bcui19/
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Retrieval, rerankers, and RAG tips and tricks | Data Brew | Episode 39
In this episode, Andrew Drozdov, Research Scientist at Databricks, explores how Retrieval Augmented Generation (RAG) enhances AI models by integrating retrieval capabilities for improved response accuracy and relevance.Highlights include:- Addressing LLM limitations by injecting relevant external information.- Optimizing document chunking, embedding, and query generation for RAG.- Improving retrieval systems with embeddings and fine-tuning techniques.- Enhancing search results using re-rankers and retrieval diagnostics.- Applying RAG strategies in enterprise AI for domain-specific improvements.
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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