Ben Lorica previously founded and hosted a biweekly podcast called the O’Reilly Data Show. Episode 136 marked the end of Ben’s run as host.
It was a series of informal conversations with thought leaders, researchers, practitioners, and writers on a wide range of topics in technology, science, and of course big data, data science, artificial intelligence, and related applications. 
The feedback was largely positive and it made a few “top technology podcasts” lists.
Here were the episodes in reverse chronological order:
- Episode 136, Peter Bailis: Machine learning for operational analytics and business intelligence
- Episode 135, Arun Kejariwal and Ira Cohen: Machine learning and analytics for time series data
- Episode 134, Michael Mahoney: Understanding deep neural networks
- Episode 133, Kesha Williams: Becoming a machine learning practitioner
- Episode 132, Alex Ratner: Labeling, transforming, and structuring training data sets for machine learning
- Episode 131, Cassie Kozyrkov: Make data science more useful
- Episode 130, Roger Chen: Acquiring and sharing high-quality data
- Episode 129, Guest appearance on Software Engineering Daily: Tools for machine learning development
- Episode 128, Nick Pentreath: Enabling end-to-end machine learning pipelines in real-world applications
- Episode 127, Dhruba Borthakur and Shruti Bhat: Bringing scalable real-time analytics to the enterprise
- Episode 126, Jike Chong: Applications of data science and machine learning in financial services
- Episode 125, Jeff Jonas: Real-time entity resolution made accessible
- Episode 124, Neelesh Salian: Why companies are in need of data lineage solutions
- Episode 123, Avner Braverman: What data scientists and data engineers can do with current generation serverless technologies
- Episode 122, Forough Poursabzi Sangdeh: It’s time for data scientists to collaborate with researchers in other disciplines
- Episode 121, Kartik Hosanagar: Algorithms are shaping our lives – here’s how we wrest back control
- Episode 120, P.W. Singer: Why your attention is like a piece of contested territory
- Episode 119, Siwei Lyu: The technical, societal, and cultural challenges that come with the rise of fake media
- Episode 118, Maryam Jahanshahi: Using machine learning and analytics to attract and retain employees
- Episode 117, Andrew Burt: How machine learning impacts information security
- Episode 116, Haoyuan Li: In the age of AI, fundamental value resides in data
- Episode 115, Special episode to mark the end-of-2018: Trends in data, machine learning, and AI
- Episode 114, Alex Wong: Tools for generating deep neural networks with efficient network architectures
- Episode 113, Vitaly Gordon: Building tools for enterprise data science
- Episode 112, Francesca Lazzeri and Jaya Mathew: Lessons learned while helping enterprises adopt machine learning
- Episode 111, Alon Kaufman: Machine learning on encrypted data
- Episode 110, Jacob Ward: How social science research can inform the design of AI systems
- Episode 109, Sharad Goel and Sam Corbett-Davies: Why it’s hard to design fair machine learning models
- Episode 108, Alan Nichol: Using machine learning to improve dialog flow in conversational applications
- Episode 107, Eric Jonas: Building accessible tools for large-scale computation and machine learning
- Episode 106, Harish Doddi: Simplifying machine learning lifecycle management
- Episode 105, Chang Liu: How privacy-preserving techniques can lead to more robust machine learning models
- Episode 104, Andrew Feldman: Specialized hardware for deep learning will unleash innovation
- Episode 103, Aurélie Pols: Data regulations and privacy discussions are still in the early stages
- Episode 102, Andrew Burt and Steven Touw: Managing risk in machine learning models
- Episode 101, Ashok Srivastava: The real value of data requires a holistic view of the end-to-end data pipeline
- Episode 100, Special show to mark our 100th episode: The evolution of data science, data engineering, and AI
- Episode 99, Jason Dai: Companies in China are moving quickly to embrace AI technologies
- Episode 98, Jerry Overton: Teaching and implementing data science and AI in the enterprise
- Episode 97, Guillaume Chaslot: The importance of transparency and user control in machine learning
- Episode 96, Jesse Anderson and Paco Nathan: What machine learning engineers need to know
- Episode 95, Ameet Talwalkar: How to train and deploy deep learning at scale
- Episode 94, Ofer Ronen: Using machine learning to monitor and optimize chatbots
- Episode 93, Danny Lange: Unleashing the potential of reinforcement learning
- Episode 92, Leo Meyerovich: Graphs as the front end for machine learning
- Episode 91, Mark Hammond: Machine learning needs machine teaching
- Episode 90, Fabian Yamaguchi: How machine learning can be used to write more secure computer programs
- Episode 89, Kris Hammond: Bringing AI into the enterprise
- Episode 88, Tim Kraska: How machine learning will accelerate data management systems
- Episode 87, Christine Hung: Machine learning at Spotify – You are what you stream
- Episode 86, Neha Narkhede: The current state of Apache Kafka
- Episode 85, David Talby: Building a natural language processing library for Apache Spark
- Episode 84, Rhea Liu: Machine intelligence for content distribution, logistics, smarter cities, and more
- Episode 83, Bruno Fernandez-Ruiz: Vehicle-to-vehicle communication networks can help fuel smart cities
- Episode 82, Carme Artigas: Transforming organizations through analytics centers of excellence
- Episode 81, Ion Stoica and Matei Zaharia: The state of machine learning in Apache Spark
- Episode 80, Kenneth Stanley: Effective mechanisms for searching the space of machine learning algorithms
- Episode 79, Robert Nishihara and Philipp Moritz: How Ray makes continuous learning accessible and easy to scale
- Episode 78, Soumith Chintala: Why AI and machine learning researchers are beginning to embrace PyTorch
- Episode 77, Evangelos Simoudis: How big data and AI will reshape the automotive industry
- Episode 76, Grace Huang: A framework for building and evaluating data products
- Episode 75, Naveen Rao: Building a next-generation platform for deep learning
- Episode 74, Michael Freedman: A scalable time-series database that supports SQL
- Episode 73, Geoffrey Bradway: Programming collective intelligence for financial trading
- Episode 72, Alex Ratner: Creating large training data sets quickly
- Episode 71, Jeremy Stanley: Data science and deep learning in retail
- Episode 70, David Ferrucci: Language understanding remains one of AI’s grand challenges
- Episode 69, Lukas Biewald: Data preparation in the age of deep learning
- Episode 68, Reza Zadeh: Scaling Machine Learning
- Episode 67, Karthik Ramasamy: Architecting and building end-to-end streaming applications
- Episode 66, Aurélien Géron: Becoming a machine learning engineer
- Episode 65, Francisco Webber: Natural language analysis using Hierarchical Temporal Memory
- Episode 63, Anima Anandkumar: Deep learning that’s easy to implement and easy to scale
- Episode 62, Parvez Ahammad: Building machine learning solutions that can withstand adversarial attacks
- Episode 61, Jason Dai: Deep learning for Apache Spark
- Episode 60, Adam Gibson: The key to building deep learning solutions for large enterprises
- Episode 59, Greg Diamos: How big compute is powering the deep learning rocket ship
- Episode 58, end of 2016 episode: 2017 will be the year the data science and big data community engage with AI technologies
- Episode 57, Ion Stoica: Data is only as valuable as the decisions it enables
- Episode 56, Vikash Mansinghka: Introducing model-based thinking into AI systems
- Episode 55, Michael Franklin on the lasting legacy of AMPLab
- Episode 54, Dafna Shahaf: Visual tools for overcoming information overload
- Episode 53, Christopher Nguyen: Why businesses should pay attention to deep learning
- Episode 51, Shaoshan Liu: The technology behind self-driving vehicles
- Episode 50, Dean Wampler: Data architectures for streaming applications
- Episode 49, Michael Li: Data science for humans and data science for machines
- Episode 48, Rana el Kaliouby: The importance of emotion in AI systems
- Episode 47, Adam Marcus: Building human-assisted AI applications
- Episode 46, Jana Eggers: Enabling enterprise adoption of AI technologies
- Episode 45, John Akred: Using Agile development techniques for data science projects
- Episode 44, Yishay Carmiel: Commercial speech recognition systems in the age of big data and deep learning
- Episode 43, Rajat Monga: Building intelligent applications with deep learning and TensorFlow (a conversation with the co-creator of TensorFlow)
- Episode 42, Rohit Jain: Hybrid transactional/analytic systems and the quest for database nirvana
- Episode 41, Mike Tung: Using AI to build a comprehensive database of knowledge
- Episode 40, Michael Armbrust: Structured streaming comes to Apache Spark 2.0
- Episode 39, Danny Bickson: Recent trends in recommender systems
- Episode 38, Ira Cohen: Semi-supervised, unsupervised, and adaptive algorithms for large-scale time series
- Episode 37, Mikio Braun: Practical machine learning techniques for building intelligent applications
- Episode 36, Duncan Ross: Democratizing business analytics
- Episode 35, M.C. Srivas: Stream processing and messaging systems for the IoT age
- Episode 34, Fang Yu: Using Apache Spark to predict attack vectors among billions of users and trillions of events
- Episode 33, Joe Hellerstein: Metadata services can lead to performance and organizational improvements
- Episode 32, Eric Colson: Building a business that combines human experts and data science
- Episode 31, Vasant Dhar: Is 2016 the year you let robots manage your money?
- Episode 30, Ben Horowitz and Reynold Xin: Investing in big data technologies (special end of 2015 episode)
- Episode 29, Evan Chan: Building a scalable platform for streaming updates and analytics
- Episode 28, Emil Eifrem: Graph databases are powering mission-critical applications
- Episode 27, Jai Ranganathan: architecting big data applications in the cloud
- Episode 26, Tyler Akidau: Building systems for massive scale data applications
- Episode 25, Evangelos Simoudis: Turning big data into actionable insights
- Episode 24, Todd Lipcon: Resolving transactional access and analytic performance trade-offs
- Episode 23, Dean Wampler: Building enterprise data applications with open source components
- Episode 22, Mike Cafarella: From search to distributed computing to large-scale information extraction (Mike is the co-founder of Hadoop, Nutch, and Lattice Data)
- Episode 21, Alice Zheng: Bridging the divide – Business users and machine learning experts
- Episode 20, David Epstein: Pattern recognition and sports data
- Episode 19, Poppy Crum: Understanding neural function and virtual reality
- Episode 18, My conversation with Ben Recht led to a post on the 6 reasons why I like KeystoneML
- Episode 17, Ihab Ilyas: Why data preparation frameworks rely on human-in-the-loop systems
- Episode 16, Phil Liu: Building self-service tools to monitor high-volume time-series data
- Episode 15, Patrick Wendell on Apache Spark: Powering applications on-premise and in the cloud
- Episode 14, Gary Kazantsev: Data science makes an impact on Wall Street (the accompanying post has been translated into Chinese)
- Episode 13, Anima Anandkumar: The tensor renaissance in data science
- Episode 12, Michael Stack: Coming full circle with Bigtable and HBase
- Episode 11, Mikio Braun: Building big data systems in academia and industry
- Episode 10, Erich Nachbar: Redefining power distribution using big data
- Episode 9, Angie Ma: Turning Ph.D.s into industrial data scientists and data engineers
- Episode 8, David Blei on Topic models: Past, present, and future
- Episode 7, Kira Radinsky: Forecasting events, from disease outbreaks to sales to cancer research
- Episode 6, Carlos Guestrin: The evolution of GraphLab
- Episode 5, DJ Patil: A brief look at data science’s past and future
- Episode 4, Ion Stoica: Apache Spark’s journey from academia to industry
- Episode 3, Sarah Meiklejohn: Clustering bitcoin accounts using heuristics
- Episode 2, Jay Kreps: Building Apache Kafka from scratch
- Episode 1, Rajiv Maheswaran: The Science of moving dots (Sports Analytics)