Data Brew Season 3 Episode 6: Open Source

Data Brew by Databricks

#Databricks #Data Analytics #Apache Spark #Delta Lake #Machine Learning #Data Engineering #Artificial Intelligence #Tech #Data Science #Science #Lifestyle #Podcasting Education

34:35

Marcia Clark, best known as the lead prosecutor in the O.J. Simpson trial, has become a trailblazer for women in law and beyond. Her journey from courtroom to bestselling author reflects her resilience and determination to redefine herself amidst intense public scrutiny. On this episode of Worth Knowing, Clark dives into her latest book, *Trial by Ambush*, which examines the 1953 Barbara Graham case—a story that highlights gender bias, media sensationalism, and the notion that all cases are subject to societal, cultural, and political winds. Clark shares how her experiences during the Simpson trial shaped her perspective on societal pressures and the role of women in high-stakes professions. Her reflections on how media, forensic science, and legal practices have evolved over decades offer valuable insights into the intersection of law and culture. This conversation is a compelling exploration of true crime, personal growth, and how Clark’s groundbreaking career continues to inspire a new generation of women to challenge norms and forge their own paths. Marcia Clark is a bestselling author and a criminal lawyer who began her career in law as a criminal defense attorney and went on to become a prosecutor in the L.A. District Attorney's Office in 1981. She spent ten years in the Special Trials Unit, where she handled a number of high-profile cases, including the prosecution of stalker/murderer Robert Bardo, whose conviction for the murder of actress Rebecca Schaeffer resulted in legislation that offered victims better protection from stalkers as well as increased punishment for the offenders. She was lead prosecutor for the O.J. Simpson murder trial. In May of 1997 her book on the Simpson case, "Without a Doubt," was published and reached #1 on the New York Times, Wall St. Journal, Washington Post, Los Angeles Times, and Publishers Weekly bestsellers lists. In February 2016, Clark re-released the book with a new foreword. Resources Sign up for the Worth Knowing LinkedIn Newsletter to stay up to date: https://www.linkedin.com/newsletters/worth-knowing-7236433935503618048/ Follow Bonnie on LinkedIn: https://www.linkedin.com/in/bonnie-habyan/ Go to the Worth Knowing website: https://www.worthknowing.show Check out The Agile Brand Guide website with articles, insights, and Martechipedia, the wiki for marketing technology: https://www.agilebrandguide.com The Agile Brand is produced by Missing Link—a Latina-owned strategy-driven, creatively fueled production co-op. From ideation to creation, they craft human connections through intelligent, engaging and informative content. https://www.missinglink.company…

4 years ago 33:49

MP3•Episode home

For our third season, we focus on how leaders use data for change. Whether it’s building data teams or using data as a constructive catalyst, we interview subject matter experts from industry to dive deeper into these topics.
For our season 3 finale, Nithya Ruff discusses the open-source ecosystem, ways to contribute to open-source projects (hint: it’s not just about the code), and how businesses can balance community and company interests. With 95% of open-source contributions coming from men, Nithya also educates us on how to improve diversity & inclusion in the open-source community.

See more at databricks.com/data-brew

43 episodes

Data Brew Season 3 Episode 6: Open Source

Data Brew by Databricks

73 subscribers

published 4 years ago

MP3•Episode home

See more at databricks.com/data-brew

43 episodes

#Databricks #Data Analytics #Apache Spark #Delta Lake #Machine Learning #Data Engineering #Artificial Intelligence #Tech #Data Science #Science #Lifestyle #Podcasting Education

All episodes

1
Benchmarking Domain Intelligence | Data Brew | Episode 45 31:41

28 days ago31:41

31:41

In this episode, Pallavi Koppol, Research Scientist at Databricks, explores the importance of domain-specific intelligence in large language models (LLMs). She discusses how enterprises need models tailored to their unique jargon, data, and tasks rather than relying solely on general benchmarks. Highlights include: - Why benchmarking LLMs for domain-specific tasks is critical for enterprise AI. - An introduction to the Databricks Intelligence Benchmarking Suite (DIBS). - Evaluating models on real-world applications like RAG, text-to-JSON, and function calling. - The evolving landscape of open-source vs. closed-source LLMs. - How industry and academia can collaborate to improve AI benchmarking.…

1
SWE-bench & SWE-agent | Data Brew | Episode 44 36:22

5 weeks ago36:22

36:22

In this episode, Kilian Lieret, Research Software Engineer, and Carlos Jimenez, Computer Science PhD Candidate at Princeton University, discuss SWE-bench and SWE-agent, two groundbreaking tools for evaluating and enhancing AI in software engineering. Highlights include: - SWE-bench: A benchmark for assessing AI models on real-world coding tasks. - Addressing data leakage concerns in GitHub-sourced benchmarks. - SWE-agent: An AI-driven system for navigating and solving coding challenges. - Overcoming agent limitations, such as getting stuck in loops. - The future of AI-powered code reviews and automation in software engineering.…

1
Enterprise AI: Research to Product | Data Brew | Episode 43 38:03

6 weeks ago38:03

38:03

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.…

1
Multimodal AI | Data Brew | Episode 42 42:14

6 weeks ago42:14

42:14

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.…

1
Age of Agents | Data Brew | Episode 41 40:47

8 weeks ago40:47

40:47

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.…

1
Reward Models | Data Brew | Episode 40 39:58

9 weeks ago39:58

39:58

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 Preference Optimization (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/…

1
Retrieval, rerankers, and RAG tips and tricks | Data Brew | Episode 39 45:22

13 weeks ago45:22

45:22

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.…

1
The Power of Synthetic Data | Data Brew | Episode 38 42:28

15 weeks ago42:28

42:28

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.…

1
Secret to Production AI: Tools & Infrastructure | Data Brew | Episode 37 37:14

17 weeks ago37:14

37:14

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.…

1
Mixture of Memory Experts (MoME) | Data Brew | Episode 36 41:24

19 weeks ago41:24

41:24

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/…

1
Mixed Attention & LLM Context | Data Brew | Episode 35 39:11

26 weeks ago39:11

39:11

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.…

1
Kumo AI & Relational Deep Learning | Data Brew | Episode 34 43:27

31 weeks ago43:27

43:27

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.…

1
LLMs: Internals, Hallucinations, and Applications | Data Brew | Episode 33 38:50

2 years ago38:50

38:50

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).…

1
Demonstrate–Search–Predict Framework | Data Brew | Episode 32 33:14

2 years ago33:14

33:14

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.…

1
Generative AI Risks | Data Brew | Episode 31 34:38

2 years ago34:38