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 ex...
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.
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The Power of Synthetic Data | Data Brew | Episode 38
In this episode, Yev Meyer, Chief Scientist at Gretel AI, explores how synthetic data transforms AI and ML by improving data access, quality, privacy, and model training.Highlights include:- Leveraging synthetic data to overcome AI data limitations.- Enhancing model training while mitigating ethical and privacy risks.- Exploring the intersection of computational neuroscience and AI workflows.- Addressing licensing and legal considerations in synthetic data usage.- Unlocking private datasets for broader and safer AI applications.
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Secret to Production AI: Tools & Infrastructure | Data Brew | Episode 37
In this episode, Julia Neagu, CEO & co-founder of Quotient AI, explores the challenges of deploying Generative AI and LLMs, focusing on model evaluation, human-in-the-loop systems, and iterative development.Highlights include:- Merging reinforcement learning and unsupervised learning for real-time AI optimization.- Reducing bias in machine learning with fairness and ethical considerations.- Lessons from large-scale AI deployments on scalability and feedback loops.- Automating workflows with AI through successful business examples.- Best practices for managing AI pipelines, from data collection to validation.
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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