Amber Case covers methods product designers and managers can use to improve interactions through an understanding of sound design.
Hilary Mason explores the current state of AI and ML and what’s coming next in applied ML.
Ben Sharma shares how the best organizations immunize themselves against the plague of static data and rigid process
Julia Angwin discusses what she's learned about forgiveness from her series of articles on algorithmic accountability and the lessons we all need to learn for the coming AI future.
Dinesh Nirmal explains how AI is helping supply school lunch and keep ahead of regulations.
Jacob Ward reveals the relationship between the unconscious habits of our minds and the way that AI is poised to amplify them, alter them, maybe even reprogram them.
Brain-based human-machine interfaces: New developments, legal and ethical issues, and potential uses
Amanda Pustilnik highlights potential applications of data from new technologies that capture brain-based processes.
The O’Reilly Data Show Podcast: Alan Nichol on building a suite of open source tools for chatbot developers.
Cassie Kozyrkov explores why businesses fail at machine learning despite its tremendous potential and excitement.
Ben Lorica offers an overview of recent tools for building privacy-preserving and secure machine learning products and services.
DD Dasgupta explores the edge-cloud continuum, explaining how the roles of data centers and cloud infrastructure are redefined through the mainstream adoption of AI, ML, and IoT technologies.
Ted Dunning discusses how new tools can change the way production systems work.
Executives from Cloudera and PNC Bank look at the challenges posed by data-hungry organizations.
Watch highlights from expert talks covering data science, machine learning, algorithmic accountability, and more.
Drew Paroski and Aatif Din share how to develop modern database applications without sacrificing cost savings, data familiarity, and flexibility.
It has become much more feasible to run high-performance data platforms directly inside Kubernetes.
If we’re going to think about the ethics of data and how it’s used, then we have to take into account how data flows.
This collection of data governance resources will get you up to speed on the basics and best practices.
The O’Reilly Data Show Podcast: Eric Jonas on Pywren, scientific computation, and machine learning.
Fernando Perez talks about UC Berkeley's transition into an environment where many undergraduates use Jupyter and the open data ecosystem as naturally as they use email.
Tracy Teal explains how to bring people to data and empower them to address their questions.
Michelle Ufford shares how Netflix leverages notebooks today and describes a brief vision for the future.
Michelle Gill discusses how data science methods and tools can link information from different scientific fields and accelerate discovery.
Cristian Capdevila explains how Prognos is predicting disease.
Ryan Abernathey makes the case for the large-scale migration of scientific data and research to the cloud.
David Schaaf explains how data science and data engineering can work together to deliver results to decision makers.
Watch keynotes covering Jupyter's role in business, data science, higher education, open source, journalism, and other domains, from JupyterCon in New York 2018.
Carol Willing shows how Jupyter's challenges can be addressed by embracing complexity and trusting others.
Dan Romuald Mbanga walks through the ecosystem around the machine learning platform and API services at AWS.
Julia Meinwald outlines effective ways to support the unseen labor maintaining a healthy open source ecosystem.
Luciano Resende explores some of the open source initiatives IBM is leading in the Jupyter ecosystem.
Paco Nathan shares a few unexpected things that emerged in Jupyter in 2018.
Mark Hansen explains how computation has forever changed the practice of journalism.
All the cool kids are doing it, maybe we should too? Jupyter, gravitational waves, and the LIGO and Virgo Scientific Collaborations
Will Farr offers lessons about the many advantages and few disadvantages of using Jupyter for global scientific collaborations.
The O’Reilly Data Show Podcast: Harish Doddi on accelerating the path from prototype to production.
The deployment of big data tools is being held back by the lack of standards in a number of growth areas.
New survey results highlight the ways organizations are handling machine learning's move to the mainstream.
These studies provide a foundation for discussing ethical issues so we can better integrate data ethics in real life.
The O’Reilly Data Show Podcast: Chang Liu on operations research, and the interplay between differential privacy and machine learning.
We can build a future we want to live in, or we can build a nightmare. The choice is up to us.
Five framing guidelines to help you think about building data products.
Recognizing the interest in ML, the Strata Data Conference program is designed to help companies adopt ML across large sections of their existing operations.
The O’Reilly Data Show Podcast: Andrew Feldman on why deep learning is ushering a golden age for compute architecture.
While models and algorithms garner most of the media coverage, this is a great time to be thinking about building tools in data.
Oaths have their value, but checklists will help put principles into practice.
An overview of the challenges MLflow tackles and a primer on how to get started.
Get a basic overview of data engineering and then go deeper with recommended resources.
Data scientists, data engineers, AI and ML developers, and other data professionals need to live ethical values, not just talk about them.
The O’Reilly Data Show Podcast: Aurélie Pols on GDPR, ethics, and ePrivacy.
The importance of testing your tools, using multiple tools, and seeking consistency across various interpretability techniques.
The O’Reilly Data Show Podcast: Andrew Burt and Steven Touw on how companies can manage models they cannot fully explain.
Taking blockchain technology private for the enterprise.
The O’Reilly Data Show Podcast: Ashok Srivastava on the emergence of machine learning and AI for enterprise applications.
Considerations based on experience with Fortune 500 clients.
Why model development does not equal software development.
Christine Foster discusses how today’s academic papers turn into tomorrow’s data science.
Louise Beaumont explores the five characteristics of companies that choose to succeed.
Martha Lane Fox considers the unintended consequences of technology.
Zubin Siganporia explains how the KISS principle (“Keep It Simple, Stupid”) applies to solving problems and convincing end-users to adopt data-driven solutions to their challenges.
Having worked in both research and industry, Mikio Braun shares insights into what's the same, what's different, and how deep learning might change the game.