29-Jan-26
Radar [ 18-Sep-25 11:12am ]
Generative AI in the Real World: Faye Zhang on Using AI to Improve Discovery [ 18-Sep-25 11:12am ]
In this episode, Ben Lorica and AI engineer Faye Zhang talk about discoverability: how to use AI to build search and recommendation engines that actually find what you want. Listen in to learn how AI goes way beyond simple collaborative filtering—pulling in many different kinds of data and metadata, including images and voice, to get […]
Generative AI in the Real World: Luke Wroblewski on When Databases Talk Agent-Speak [ 04-Sep-25 5:01pm ]
Join Luke Wroblewski and Ben Lorica as they talk about the future of software development. What happens when we have databases that are designed to interact with agents and language models rather than humans? We're starting to see what that world will look like. It's an exciting time to be a software developer. About the […]
27-Jun-25
Generative AI in the Real World: Stefania Druga on Designing for the Next Generation [ 26-Jun-25 11:01am ]
How do you teach kids to use and build with AI? That's what Stefania Druga works on. It's important to be sensitive to their creativity, sense of fun, and desire to learn. When designing for kids, it's important to design with them, not just for them. That's a lesson that has important implications for adults, […]
20-Jun-25
Generative AI in the Real World: Douwe Kiela on Why RAG Isn't Dead [ 12-Jun-25 10:58am ]
Join our host Ben Lorica and Douwe Kiela, cofounder of Contextual AI and author of the first paper on RAG, to find out why RAG remains as relevant as ever. Regardless of what you call it, retrieval is at the heart of generative AI. Find out why—and how to build effective RAG-based systems. About the […]
16-May-25
Machine learning for operational analytics and business intelligence [ 10-Oct-19 5:00pm ]
In this episode of the Data Show, I speak with Peter Bailis, founder and CEO of Sisu, a startup that is using machine learning to improve operational analytics. Bailis is also an assistant professor of computer science at Stanford University, where he conducts research into data-intensive systems and where he is co-founder of the DAWN […]
Machine learning and analytics for time series data [ 26-Sep-19 5:00pm ]
In this episode of the Data Show, I speak with Arun Kejariwal of Facebook and Ira Cohen of Anodot (full disclosure: I'm an advisor to Anodot). This conversation stemmed from a recent online panel discussion we did, where we discussed time series data, and, specifically, anomaly detection and forecasting. Both Kejariwal (at Machine Zone, Twitter, […]
Understanding deep neural networks [ 12-Sep-19 5:00pm ]
In this episode of the Data Show, I speak with Michael Mahoney, a member of RISELab, the International Computer Science Institute, and the Department of Statistics at UC Berkeley. A physicist by training, Mahoney has been at the forefront of many important problems in large-scale data analysis. On the theoretical side, his works spans algorithmic […]
Becoming a machine learning practitioner [ 29-Aug-19 5:00pm ]
In this episode of the Data Show, I speak with Kesha Williams, technical instructor at A Cloud Guru, a training company focused on cloud computing. As a full stack web developer, Williams became intrigued by machine learning and started teaching herself the ML tools on Amazon Web Services. Fast forward to today, Williams has built […]
Labeling, transforming, and structuring training data sets for machine learning [ 15-Aug-19 5:00pm ]
In this episode of the Data Show, I speak with Alex Ratner, project lead for Stanford's Snorkel open source project; Ratner also recently garnered a faculty position at the University of Washington and is currently working on a company supporting and extending the Snorkel project. Snorkel is a framework for building and managing training data. […]
Make data science more useful [ 01-Aug-19 5:00pm ]
In this episode of the Data Show, I speak with Cassie Kozyrkov, technical director and chief decision scientist at Google Cloud. She describes “decision intelligence” as an interdisciplinary field concerned with all aspects of decision-making, and which combines data science with the behavioral sciences. Most recently she has been focused on developing best practices that […]
Acquiring and sharing high-quality data [ 18-Jul-19 5:00pm ]
In this episode of the Data Show, I spoke with Roger Chen, co-founder and CEO of Computable Labs, a startup focused on building tools for the creation of data networks and data exchanges. Chen has also served as co-chair of O’Reilly’s Artificial Intelligence Conference since its inception in 2016. This conversation took place the day […]
Tools for machine learning development [ 03-Jul-19 5:00pm ]
In this week’s episode of the Data Show, we’re featuring an interview Data Show host Ben Lorica participated in for the Software Engineering Daily Podcast, where he was interviewed by Jeff Meyerson. Their conversation mainly centered around data engineering, data architecture and infrastructure, and machine learning (ML). Here are a few highlights: Tools for productive […]
Enabling end-to-end machine learning pipelines in real-world applications [ 20-Jun-19 5:10pm ]
In this episode of the Data Show, I spoke with Nick Pentreath, principal engineer at IBM. Pentreath was an early and avid user of Apache Spark, and he subsequently became a Spark committer and PMC member. Most recently his focus has been on machine learning, particularly deep learning, and he is part of a group […]
Bringing scalable real-time analytics to the enterprise [ 09-Jun-19 5:00pm ]
In this episode of the Data Show, I spoke with Dhruba Borthakur (co-founder and CTO) and Shruti Bhat (SVP of Product) of Rockset, a startup focused on building solutions for interactive data science and live applications. Borthakur was the founding engineer of HDFS and creator of RocksDB, while Bhat is an experienced product and marketing […]
Applications of data science and machine learning in financial services [ 23-May-19 5:00pm ]
In this episode of the Data Show, I spoke with Jike Chong, chief data scientist at Acorns, a startup focused on building tools for micro-investing. Chong has extensive experience using analytics and machine learning in financial services, and he has experience building data science teams in the U.S. and in China. We had a great […]
