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.

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.

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.

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/realsharonzhou
https://www.lamini.ai/

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.

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.

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).

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.