Data Brew by Databricks

Mixture of Memory Experts (MoME) | Data Brew | Episode 36

In this episode, Sharon Zhou, Co-Founder and CEO of Lamini AI, shares her expertise in the world of AI, focusing on fine-tuning models for improved performance and reliability.Highlights include:- The integration of determinism and probabilism for handling unstructured data and user queries effectively.- Proprietary techniques like memory tuning and robust evaluation frameworks to mitigate model inaccuracies and hallucinations.- Lessons learned from deploying AI applications, including insights from GitHub Copilot’s rollout.Connect with Sharon Zhou and Lamini:https://www.linkedin.com/in/zhousharon/https://x.com/realsharonzhouhttps://www.lamini.ai/

Mixed Attention & LLM Context | Data Brew | Episode 35

In this episode, Shashank Rajput, Research Scientist at Mosaic and Databricks, explores innovative approaches in large language models (LLMs), with a focus on Retrieval Augmented Generation (RAG) and its impact on improving efficiency and reducing operational costs.Highlights include:- How RAG enhances LLM accuracy by incorporating relevant external documents.- The evolution of attention mechanisms, including mixed attention strategies.- Practical applications of Mamba architectures and their trade-offs with traditional transformers.

Kumo AI & Relational Deep Learning | Data Brew | Episode 34

In this episode, Jure Leskovec, Co-founder of Kumo AI and Professor of Computer Science at Stanford University, discusses Relational Deep Learning (RDL) and its role in automating feature engineering. Highlights include:- How RDL enhances predictive modeling.- Applications in fraud detection and recommendation systems.- The use of graph neural networks to simplify complex data structures.

LLMs: Internals, Hallucinations, and Applications | Data Brew | Episode 33

Our fifth season dives into large language models (LLMs), from understanding the internals to the risks of using them and everything in between. While we're at it, we'll be enjoying our morning brew.In this session, we interviewed Chengyin Eng (Senior Data Scientist, Databricks), Sam Raymond (Senior Data Scientist, Databricks), and Joseph Bradley (Lead Production Specialist - ML, Databricks) on the best practices around LLM use cases, prompt engineering, and how to adapt MLOps for LLMs (i.e., LLMOps).

Demonstrate–Search–Predict Framework | Data Brew | Episode 32

We will dive into LLMs for our fifth season, from understanding the internals to the risks of using them and everything in between.  While we’re at it, we’ll be enjoying our morning brew.In this session, we interviewed Omar Khattab - Computer Science Ph.D. Student at Stanford, creator of DSP (Demonstrate–Search–Predict Framework), to discuss DSP, common applications, and the future of NLP.