What is the O’Reilly data show?
The O’Reilly Data Show podcast: The Hadoop ecosystem, the recent surge in interest in all things real time, and developments in hardware. The O’Reilly Data Show podcast: Tyler Akidau on the evolution of systems for bounded and unbounded data processing.
What are the best data science podcasts?
The O’Reilly Data Show podcast: Fang Yu on data science in security, unsupervised learning, and Apache Spark. The O’Reilly Data Show podcast: Joe Hellerstein on data wrangling, distributed systems, and metadata services. The O’Reilly Data Show podcast: Eric Colson on algorithms, human computation, and building data science teams.
What are the best podcasts on anomaly detection and forecasting?
The O’Reilly Data Show Podcast: Arun Kejariwal and Ira Cohen on building large-scale, real-time solutions for anomaly detection and forecasting. The O’Reilly Data Show Podcast: Michael Mahoney on developing a practical theory for deep learning.
Bringing scalable real-time analytics to the enterprise
The O’Reilly Data Show Podcast: Dhruba Borthakur and Shruti Bhat on enabling interactive analytics and data applications against live data.
Applications of data science and machine learning in financial services
The O’Reilly Data Show Podcast: Jike Chong on the many exciting opportunities for data professionals in the U.S. and China.
Technology Podcasts
The O'Reilly Data Show Podcast explores the opportunities and techniques driving big data, data science, and AI.
Machine learning for operational analytics and business intelligence
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
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.
Understanding deep neural networks
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.
Becoming a machine learning practitioner
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
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
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.
Using Apache Spark to predict attack vectors among billions of users and trillions of events
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Building a business that combines human experts and data science
Subscribe to the O’Reilly Data Show Podcast to explore the opportunities and techniques driving big data and data science.
Investing in big data technologies
Subscribe to the O’Reilly Data Show Podcast to explore the opportunities and techniques driving big data and data science.
Building a scalable platform for streaming updates and analytics
Subscribe to the O’Reilly Data Show Podcast to explore the opportunities and techniques driving big data and data science.
Bringing Apache Spark & Apache Cassandra together
Datastax credits me with inspiring them to bring Spark into Cassandra … I think they’re very generous about that. I think I was one of the first folks to talk about the possibility of bringing Cassandra and Spark together.
Graph databases are powering mission-critical applications
Subscribe to the O’Reilly Data Show Podcast to explore the opportunities and techniques driving big data and data science.
Graph and NoSQL databases
The relational database had been an accelerator, and here it’s really slowing us down. What we ended up concluding was that the problem was this mismatch between the shape of the data and the abstractions that were exposed by our infrastructure.