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.
O’Reilly Data Show Podcast
The O’Reilly Data Show Podcast explores the opportunities and techniques driving big data, data science, and AI. Subscribe on Apple Podcasts, Stitcher, Google Play, and RSS.
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.
Understanding deep neural networks
The O’Reilly Data Show Podcast: Michael Mahoney on developing a practical theory for deep learning.
Becoming a machine learning practitioner
The O’Reilly Data Show Podcast: Kesha Williams on how she added machine learning to her software developer toolkit.
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.
Make data science more useful
The O’Reilly Data Show Podcast: Cassie Kozyrkov on connecting data and AI to business.
Acquiring and sharing high-quality data
The O’Reilly Data Show Podcast: Roger Chen on the fair value and decentralized governance of 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.
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.
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.
Real-time entity resolution made accessible
The O’Reilly Data Show Podcast: Jeff Jonas on the evolution of entity resolution technologies.
Why companies are in need of data lineage solutions
The O’Reilly Data Show Podcast: Neelesh Salian on data lineage, data governance, and evolving data platforms.
What data scientists and data engineers can do with current generation serverless technologies
The O’Reilly Data Show Podcast: Avner Braverman on what’s missing from serverless today and what users should expect in the near future.
It’s time for data scientists to collaborate with researchers in other disciplines
The O’Reilly Data Show Podcast: Forough Poursabzi Sangdeh on the interdisciplinary nature of interpretable and interactive machine learning.
Algorithms are shaping our lives—here’s how we wrest back control
The O’Reilly Data Show Podcast: Kartik Hosanagar on the growing power and sophistication of algorithms.
Why your attention is like a piece of contested territory
The O’Reilly Data Show Podcast: P.W. Singer on how social media has changed, war, politics, and business.
The technical, societal, and cultural challenges that come with the rise of fake media
The O’Reilly Data Show Podcast: Siwei Lyu on machine learning for digital media forensics and image synthesis.
Using machine learning and analytics to attract and retain employees
The O’Reilly Data Show Podcast: Maryam Jahanshahi on building tools to help improve efficiency and fairness in how companies recruit.
How machine learning impacts information security
The O’Reilly Data Show Podcast: Andrew Burt on the need to modernize data protection tools and strategies.
In the age of AI, fundamental value resides in data
The O’Reilly Data Show Podcast: Haoyuan Li on accelerating analytic workloads, and innovation in data and AI in China.
Trends in data, machine learning, and AI
The O’Reilly Data Show Podcast: Ben Lorica looks ahead at what we can expect in 2019 in the big data landscape.
Tools for generating deep neural networks with efficient network architectures
The O’Reilly Data Show Podcast: Alex Wong on building human-in-the-loop automation solutions for enterprise machine learning.
Building tools for enterprise data science
The O’Reilly Data Show Podcast: Vitaly Gordon on the rise of automation tools in data science.
Lessons learned while helping enterprises adopt machine learning
The O’Reilly Data Show Podcast: Francesca Lazzeri and Jaya Mathew on digital transformation, culture and organization, and the team data science process.
Machine learning on encrypted data
The O’Reilly Data Show Podcast: Alon Kaufman on the interplay between machine learning, encryption, and security.
How social science research can inform the design of AI systems
The O’Reilly Data Show Podcast: Jacob Ward on the interplay between psychology, decision-making, and AI systems.
Why it’s hard to design fair machine learning models
The O’Reilly Data Show Podcast: Sharad Goel and Sam Corbett-Davies on the limitations of popular mathematical formalizations of fairness.
Using machine learning to improve dialog flow in conversational applications
The O’Reilly Data Show Podcast: Alan Nichol on building a suite of open source tools for chatbot developers.
Building accessible tools for large-scale computation and machine learning
The O’Reilly Data Show Podcast: Eric Jonas on Pywren, scientific computation, and machine learning.
Simplifying machine learning lifecycle management
The O’Reilly Data Show Podcast: Harish Doddi on accelerating the path from prototype to production.
How privacy-preserving techniques can lead to more robust machine learning models
The O’Reilly Data Show Podcast: Chang Liu on operations research, and the interplay between differential privacy and machine learning.
Specialized hardware for deep learning will unleash innovation
The O’Reilly Data Show Podcast: Andrew Feldman on why deep learning is ushering a golden age for compute architecture.
Data regulations and privacy discussions are still in the early stages
The O’Reilly Data Show Podcast: Aurélie Pols on GDPR, ethics, and ePrivacy.
Managing risk in machine learning models
The O’Reilly Data Show Podcast: Andrew Burt and Steven Touw on how companies can manage models they cannot fully explain.
The real value of data requires a holistic view of the end-to-end data pipeline
The O’Reilly Data Show Podcast: Ashok Srivastava on the emergence of machine learning and AI for enterprise applications.
The evolution of data science, data engineering, and AI
The O’Reilly Data Show Podcast: A special episode to mark the 100th episode.
Companies in China are moving quickly to embrace AI technologies
The O’Reilly Data Show Podcast: Jason Dai on the first year of BigDL and AI in China.
Teaching and implementing data science and AI in the enterprise
The O’Reilly Data Show Podcast: Jerry Overton on organizing data teams, agile experimentation, and the importance of ethics in data science.
The importance of transparency and user control in machine learning
The O’Reilly Data Show Podcast: Guillaume Chaslot on bias and extremism in content recommendations.
What machine learning engineers need to know
The O’Reilly Data Show Podcast: Jesse Anderson and Paco Nathan on organizing data teams and next-generation messaging with Apache Pulsar.
How to train and deploy deep learning at scale
The O’Reilly Data Show Podcast: Ameet Talwalkar on large-scale machine learning.
Using machine learning to monitor and optimize chatbots
The O’Reilly Data Show Podcast: Ofer Ronen on the current state of chatbots.
Unleashing the potential of reinforcement learning
The O’Reilly Data Show Podcast: Danny Lange on how reinforcement learning can accelerate software development and how it can be democratized.
Graphs as the front end for machine learning
The O’Reilly Data Show Podcast: Leo Meyerovich on building large-scale, interactive applications that enable visual investigations.
Machine learning needs machine teaching
The O’Reilly Data Show Podcast: Mark Hammond on applications of reinforcement learning to manufacturing and industrial automation.
How machine learning can be used to write more secure computer programs
The O’Reilly Data Show Podcast: Fabian Yamaguchi on the potential of using large-scale analytics on graph representations of code.
Bringing AI into the enterprise
The O’Reilly Data Show Podcast: Kris Hammond on business applications of AI technologies and educating future AI specialists.
How machine learning will accelerate data management systems
The O’Reilly Data Show Podcast: Tim Kraska on why ML will change how we build core algorithms and data structures.
Machine learning at Spotify: You are what you stream
The O’Reilly Data Show Podcast: Christine Hung on using data to drive digital transformation and recommenders that increase user engagement.
The current state of Apache Kafka
The O’Reilly Data Show Podcast: Neha Narkhede on data integration, microservices, and Kafka’s roadmap.
Building a natural language processing library for Apache Spark
The O’Reilly Data Show Podcast: David Talby on a new NLP library for Spark, and why model development starts after a model gets deployed to production.
Machine intelligence for content distribution, logistics, smarter cities, and more
The O’Reilly Data Show Podcast: Rhea Liu on technology trends in China.
Vehicle-to-vehicle communication networks can help fuel smart cities
The O’Reilly Data Show Podcast: Bruno Fernandez-Ruiz on the importance of building the ground control center of the future.
Transforming organizations through analytics centers of excellence
The O’Reilly Data Show Podcast: Carme Artigas on helping enterprises transform themselves with big data tools and technologies.
The state of machine learning in Apache Spark
The O’Reilly Data Show Podcast: Ion Stoica and Matei Zaharia explore the rich ecosystem of analytic tools around Apache Spark.
Effective mechanisms for searching the space of machine learning algorithms
The O’Reilly Data Show Podcast: Kenneth Stanley on neuroevolution and other principled ways of exploring the world without an objective.
How Ray makes continuous learning accessible and easy to scale
The O’Reilly Data Show Podcast: Robert Nishihara and Philipp Moritz on a new framework for reinforcement learning and AI applications.
Why AI and machine learning researchers are beginning to embrace PyTorch
The O’Reilly Data Show Podcast: Soumith Chintala on building a worthy successor to Torch and on deep learning within Facebook.
How big data and AI will reshape the automotive industry
The O’Reilly Data Show Podcast: Evangelos Simoudis on next-generation mobility services.
A framework for building and evaluating data products
The O’Reilly Data Show Podcast: Pinterest data scientist Grace Huang on lessons learned in the course of machine learning product launches.
Building a next-generation platform for deep learning
The O’Reilly Data Show Podcast: Naveen Rao on emerging hardware and software infrastructure for AI.
A scalable time-series database that supports SQL
The O’Reilly Data Show Podcast: Michael Freedman on TimescaleDB and scaling SQL for time-series.
Programming collective intelligence for financial trading
The O’Reilly Data Show Podcast: Geoffrey Bradway on building a trading system that synthesizes many different models.
Creating large training data sets quickly
The O’Reilly Data Show Podcast: Alex Ratner on why weak supervision is the key to unlocking dark data.
Data science and deep learning in retail
The O’Reilly Data Show Podcast: Jeremy Stanley on hiring and leading machine learning engineers to build world-class data products.
Language understanding remains one of AI’s grand challenges
The O’Reilly Data Show Podcast: David Ferrucci on the evolution of AI systems for language understanding.
Data preparation in the age of deep learning
The O’Reilly Data Show Podcast: Lukas Biewald on why companies are spending millions of dollars on labeled data sets.
Scaling machine learning
The O’Reilly Data Show Podcast: Reza Zadeh on deep learning, hardware/software interfaces, and why computer vision is so exciting.
Architecting and building end-to-end streaming applications
The O’Reilly Data Show Podcast: Karthik Ramasamy on Heron, DistributedLog, and designing real-time applications.
Becoming a machine learning engineer
The O’Reilly Data Show Podcast: Aurélien Géron on enabling companies to use machine learning in real-world products.
Natural language analysis using Hierarchical Temporal Memory
The O’Reilly Data Show Podcast: Francisco Webber on building HTM-based enterprise applications.
Saving the world—or at least the world’s scientific and government data
The O’Reilly Data Show Podcast: Max Ogden on data preservation, distributed trust, and bringing cutting-edge technology to journalism.
Deep learning that’s easy to implement and easy to scale
The O’Reilly Data Show Podcast: Anima Anandkumar on MXNet, tensor computations and deep learning, and techniques for scaling algorithms.
Building machine learning solutions that can withstand adversarial attacks
The O’Reilly Data Show Podcast: Parvez Ahammad on minimal supervision, and the importance of explainability, interpretability, and security.
Deep learning for Apache Spark
The O’Reilly Data Show Podcast: Jason Dai on BigDL, a library for deep learning on existing data frameworks.
The key to building deep learning solutions for large enterprises
The O’Reilly Data Show Podcast: Adam Gibson on the importance of ROI, integration, and the JVM.
How big compute is powering the deep learning rocket ship
The O’Reilly Data Show Podcast: Greg Diamos on building computer systems for deep learning and AI.
2017 will be the year the data science and big data community engage with AI technologies
The O’Reilly Data Show Podcast: A look at some trends we’re watching in 2017.
Data is only as valuable as the decisions it enables
The O’Reilly Data Show Podcast: Ion Stoica on building intelligent and secure applications on live data.
Introducing model-based thinking into AI systems
The O’Reilly Data Show Podcast: Vikash Mansinghka on recent developments in probabilistic programming.
Building the next-generation big data analytics stack
The O’Reilly Data Show Podcast: Michael Franklin on the lasting legacy of AMPLab.
Visual tools for overcoming information overload
The O’Reilly Data Show Podcast: Dafna Shahaf on information cartography and AI, and Sam Wang on probabilistic methods for forecasting political elections.
Why businesses should pay attention to deep learning
The O’Reilly Data Show Podcast: Christopher Nguyen on the early days of Apache Spark, deep learning for time-series and transactional data, innovation in China, and AI.
Understanding predictive analytics
The O’Reilly Data Show Podcast: Natalino Busa on developments in feature engineering and predictive techniques across industries.
The technology behind self-driving vehicles
The O’Reilly Data Show Podcast: Shaoshan Liu on perception, knowledge, reasoning, and planning for autonomous cars.
Data architectures for streaming applications
The O’Reilly Data Show Podcast: Dean Wampler on streaming data applications, Scala and Spark, and cloud computing.
Data science for humans and data science for machines
The O’Reilly Data Show Podcast: Michael Li on the state of data engineering and data science training programs.
The importance of emotion in AI systems
The O’Reilly Data Show Podcast: Rana el Kaliouby on deep learning, emotion detection, and user engagement in an attention economy.
Building human-assisted AI applications
The O’Reilly Data Show Podcast: Adam Marcus on intelligent systems and human-in-the-loop computing.
Enabling enterprise adoption of AI technologies
The O’Reilly Data Show Podcast: Jana Eggers on building applications that rely on synaptic intelligence.
Using Agile development techniques for data science projects
The O’Reilly Data Show Podcast: John Akred on building data platforms and enterprise data strategies.
Commercial speech recognition systems in the age of big data and deep learning
The O’Reilly Data Show Podcast: Yishay Carmiel on applications of deep learning in text and speech.
Building intelligent applications with deep learning and TensorFlow
The O’Reilly Data Show Podcast: Rajat Monga on the current state of TensorFlow and training large-scale deep neural networks.
Hybrid transactional/analytic systems and the quest for database nirvana
The O’Reilly Data Show Podcast: Rohit Jain on the challenges of hybrid data management systems.
Using AI to build a comprehensive database of knowledge
The O’Reilly Data Show Podcast: Mike Tung on large-scale structured data extraction, intelligent systems, and the importance of knowledge databases.
Structured streaming comes to Apache Spark 2.0
The O’Reilly Data Show Podcast: Michael Armbrust on enabling users to perform streaming analytics, without having to reason about streaming.
Building and deploying large-scale machine learning applications
The O’Reilly Data Show Podcast: Danny Bickson on recommenders, data science, and applications of machine learning.
Semi-supervised, unsupervised, and adaptive algorithms for large-scale time series
The O’Reilly Data Show Podcast: Ira Cohen on developing machine learning tools for a broad range of real-time applications.
Practical machine learning techniques for building intelligent applications
The O’Reilly Data Show Podcast: Mikio Braun on practical data science, deep neural networks, machine learning, and AI.
Democratizing business analytics
The O’Reilly Data Show Podcast: Duncan Ross on the evolution of analytics, data mining, and data philanthropy.
Stream processing and messaging systems for the IoT age
The O’Reilly Data Show podcast: M.C. Srivas on streaming, enterprise grade systems, the Internet of Things, and data for social good.
Using Apache Spark to predict attack vectors among billions of users and trillions of events
The O’Reilly Data Show podcast: Fang Yu on data science in security, unsupervised learning, and Apache Spark.
Metadata services can lead to performance and organizational improvements
The O’Reilly Data Show podcast: Joe Hellerstein on data wrangling, distributed systems, and metadata services.
Building a business that combines human experts and data science
The O’Reilly Data Show podcast: Eric Colson on algorithms, human computation, and building data science teams.
Is 2016 the year you let robots manage your money?
The O’Reilly Data Show podcast: Vasant Dhar on the race to build “big data machines” in financial investing.
Investing in big data technologies
The O’Reilly Data Show podcast: A fireside chat with Ben Horowitz, plus Reynold Xin on the rise of Apache Spark in China.
Building a scalable platform for streaming updates and analytics
The O’Reilly Data Show podcast: Evan Chan on the early days of Spark+Cassandra, FiloDB, and cloud computing.
Graph databases are powering mission-critical applications
The O’Reilly Data Show Podcast: Emil Eifrem on popular applications of graph technologies, cloud computing, and company culture.
Jai Ranganathan on architecting big data applications in the cloud
The O’Reilly Data Show podcast: The Hadoop ecosystem, the recent surge in interest in all things real time, and developments in hardware.
Building systems for massive scale data applications
The O’Reilly Data Show podcast: Tyler Akidau on the evolution of systems for bounded and unbounded data processing.
Turning big data into actionable insights
The O’Reilly Data Show podcast: Evangelos Simoudis on data mining, investing in data startups, and corporate innovation.
Resolving transactional access and analytic performance trade-offs
The O’Reilly Data Show podcast: Todd Lipcon on hybrid and specialized tools in distributed systems.
Building enterprise data applications with open source components
The O’Reilly Data Show podcast: Dean Wampler on bounded and unbounded data processing and analytics.
From search to distributed computing to large-scale information extraction
The O'Reilly Data Show Podcast: Mike Cafarella on the early days of Hadoop/HBase and progress in structured data extraction.
6 reasons why I like KeystoneML
The O'Reilly Data Show Podcast: Ben Recht on optimization, compressed sensing, and large-scale machine learning pipelines.
Building self-service tools to monitor high-volume time-series data
The O'Reilly Data Show Podcast: Phil Liu on the evolution of metric monitoring tools and cloud computing.
Data science makes an impact on Wall Street
The O'Reilly Data Show Podcast: Gary Kazantsev on how big data and data science are making a difference in finance.
The tensor renaissance in data science
The O'Reilly Data Show Podcast: Anima Anandkumar on tensor decomposition techniques for machine learning.
Building big data systems in academia and industry
The O'Reilly Data Show Podcast: Mikio Braun on stream processing, academic research, and training.
Turning Ph.D.s into industrial data scientists
Angie Ma's startup, London-based ASI, runs a carefully structured “finishing school” for science and engineering doctorates.
Topic models: Past, present, and future
David Blei, co-creator of one of the most popular tools in text mining and machine learning, discusses the origins and applications of topic models.
A brief look at data science’s past and future
In this O'Reilly Data Show Podcast: DJ Patil weighs in on a wide range of topics in data science and big data.
Apache Spark’s journey from academia to industry
In this O'Reilly Data Show Podcast: Ion Stoica talks about the rise of Apache Spark and Apache Mesos.