Tag Archives: podcast

Machine learning for operational analytics and business intelligence

The O’Reilly Data Show Podcast: Peter Bailis on data management, ML benchmarks, and building next-gen tools for analysts. 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 scienceContinue reading “Machine learning for operational analytics and business intelligence”

Machine learning and analytics for time series data

The O’Reilly Data Show Podcast: Arun Kejariwal and Ira Cohen on building large-scale, real-time solutions for anomaly detection and forecasting. In this episode of the Data Show, I speak with Arun Kejariwalof Facebook and Ira Cohenof Anodot(full disclosure: I’m an advisor to Anodot). This conversation stemmed from a recent online panel discussion we did, whereContinue reading “Machine learning and analytics for time series data”

Understanding deep neural networks

The O’Reilly Data Show Podcast: Michael Mahoney on developing a practical theory for deep learning. 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 manyContinue reading “Understanding deep neural networks”

Becoming a machine learning practitioner

The O’Reilly Data Show Podcast: Kesha Williams on how she added machine learning to her software developer toolkit. 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 learningContinue reading “Becoming a machine learning practitioner”

Labeling, transforming, and structuring training data sets for machine learning

The O’Reilly Data Show Podcast: Alex Ratner on how to build and manage training data with Snorkel. 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 companyContinue reading “Labeling, transforming, and structuring training data sets for machine learning”

Make data science more useful

The O’Reilly Data Show Podcast: Cassie Kozyrkov on connecting data and AI to business. 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 withContinue reading “Make data science more useful”

Acquiring and sharing high-quality data

The O’Reilly Data Show Podcast: Roger Chen on the fair value and decentralized governance of data. 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-chairContinue reading “Acquiring and sharing high-quality data”

Tools for machine learning development

The O’Reilly Data Show: Ben Lorica chats with Jeff Meyerson of Software Engineering Daily about data engineering, data architecture and infrastructure, and machine learning. By Jenn Webb. 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 interviewedContinue reading “Tools for machine learning development”

Enabling end-to-end machine learning pipelines in real-world applications

The O’Reilly Data Show Podcast: Nick Pentreath on overcoming challenges in productionizing machine learning models. 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 focusContinue reading “Enabling end-to-end machine learning pipelines in real-world applications”