Data Brew by Databricks

• 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.

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• 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/

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• 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.

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• 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.

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