Untangling data pipelines with a streaming platform.
Become more agile with business intelligence and data analytics.
How human-in-the-loop data analytics is accelerating the discovery of insights.
The O’Reilly Data Show Podcast: Bruno Fernandez-Ruiz on the importance of building the ground control center of the future.
The O’Reilly Podcast: Achieving greater reliability and security when integrating data.
The O'Reilly Podcast: Gary Orenstein on developing a data infrastructure that enables the latest applications in machine learning and AI.
Utilizing GPU power to improve performance and agility.
The secret sauce for survival relies on extracting the value of data analytics.
Manuela Veloso provides an overview of the CoBot mobile service robots and their symbiotic autonomy, which lets the robots ask for help to overcome their limitations.
Joseph Sirosh shares a story about a volunteer’s dilemma, an engineer’s ingenuity, and how AI, cloud, data, and devices came together to save snow leopards.
There are endless possibilities when we connect the unconnected. Raghunath Nambiar discusses the magnitude of challenges and opportunities across industry segments.
Joanna Bryson says AI's main threat is not that it will do anything to us, but that we'll use it to predict and manipulate our behaviors.
Chad W. Jennings walks through a serverless big data architecture on Google Cloud that helps unravel the mysteries of human emotion.
Tanvi Singh asks: Do long-standing, non-Internet companies have the evidence-driven culture and platforms needed to get benefits from big data tools?
Robin Thottungal discusses the EPA's digital transformation and how it's leading to a better understanding of the interdependencies between our air, water, land, and public health.
danah boyd explores how systems are being gamed, how data is vulnerable, and what we need to do to build technical antibodies.
Tim O'Reilly says entrepreneurs need to set their sights on how we can use big data, sensors, and AI to create amazing human experiences and the economy of the future.
Terry Kline and Mike Olson look at how machine learning and predictive analytics keep more than 300,000+ connected vehicles rolling
The O’Reilly Data Show Podcast: Carme Artigas on helping enterprises transform themselves with big data tools and technologies.
Ben Lorica discusses the state of machine learning.
Manuel García-Herranz explains how to apply advances in data science, complex systems, artificial intelligence, and computational sociology to help the most vulnerable.
Anil Gadre shares how MapR customers using a converged data platform are creating new apps for the enterprise.
Sam Lavigne offers an overview of White Collar Crime Risk Zones, a predictive policing application that predicts financial crime at the city-block level.
Ziya Ma explains how Intel is driving a holistic approach to powering advanced analytics and artificial intelligence workloads.
Ever wondered how Spotify seems to know what you want? Christine Hung shares how Spotify uses data and algorithms to improve user experience and drive business.
Cesar Delgado joins Mike Olson to show how Apple is using its big data stack and expertise to solve non-data problems.
Jer Thorp talks about swimming upstream to the point where data becomes data.
Watch highlights covering data science, data engineering, data-driven business, and more. From Strata Data Conference in New York 2017.
Nikita Shamgunov explores the future of databases for fast-learning adaptable applications.
Probabilistic computation holds too much promise for it to be stifled by playing zero sum games with data.
Learn how to add big data to your organization's business processes.
A deep dive into Uber's engineering effort to optimize geospatial queries in Presto.
The O'Reilly Podcast: Dave Cassel on building a unified enterprise database to store and query any type of data.
Learn the difference between live and streaming anomaly detection systems and how to address the challenges different data velocities pose.
Learn about some of the common issues you will encounter when developing algorithms for a modern anomaly detection system.
See examples of the many traps you can fall into if you use off-the-shelf anomaly detection techniques.
The O’Reilly Data Show Podcast: Ion Stoica and Matei Zaharia explore the rich ecosystem of analytic tools around Apache Spark.
Learn to identify problems that may indicate data team dysfunction.
Learn some of the benefits of using real-time processing of data for some use cases.
6 lessons learned to get a quick start on productivity.
A look at the Layer API, TFLearn, and Keras.
Building a production-grade real-time image classification system.
Applications of CNNs for real-time image classification in the enterprise.
Why machine learning needs real-time data infrastructure.
The O’Reilly Data Show Podcast: Kenneth Stanley on neuroevolution and other principled ways of exploring the world without an objective.
Jeremy Freeman describes a growing ecosystem of scientific solutions, many of which involve Jupyter.
William Merchan shares fundamental trends driving the adoption of Jupyter and its deployment in large organizations.
Nadia Eghbal explores how money can support open source development without changing its incentives.
Brett Cannon looks at how healthy expectations can maintain a balanced relationship between open source users and project maintainers.
Lorena Barba explores how we can build a capacity to support reproducible research into the design of tools like Jupyter.
Andrew Odewahn explains how O’Reilly Media applied the Jupyter architecture to create the next generation of technical content.
Wes McKinney makes the case for a shared infrastructure for data science.
Labz ‘N Da Wild 2.0: Teaching signal and data processing at scale using Jupyter notebooks in the cloud
Demba Ba explains how he designed and implemented two Harvard courses that use cloud-based Jupyter notebooks.
Fernando Perez explains how Project Jupyter fits into a vision of collaborative development of tools that are applicable to research, education, and industry.
Rachel Thomas shares her experience using Jupyter notebooks to help students understand deep learning through experimentation.
Peter Wang talks about the co-evolution of Jupyter and Anaconda and looks at what’s needed to sustain an open and innovative future.
Watch highlights covering Jupyter notebooks, data management, collaborative data science, and more. From JupyterCon in New York 2017.
Recent trends in practical use and a discussion of key bottlenecks in supervised machine learning.
The toughest part of machine learning with Spark isn't what you think it is.
Human-guided ML pipelines for data unification and cleaning might be the only way to provide complete and trustworthy data sets for effective analytics.