Five Questions for Dinesh Dutt on the changing relationship between network and computer.
How to understand machine learning adoption in the enterprise.
Ring stacking games. With computers.
Giving context to code, human-in-the-loop design pattern, and collaborative documents.
Five Questions for Laine Campbell about building dependable databases.
Five Questions for Camille Fournier about the challenges engineers face when transitioning to managers, and how to foster great technical leadership.
Drawing with AI, Apple AI API, United Nations and AI for good, and smart oil and gas.
The O’Reilly Design Podcast: The sombrero-shaped designer, leading design teams, and designing for retail.
The O’Reilly Data Show Podcast: Alex Ratner on why weak supervision is the key to unlocking dark data.
Overcome three types of debt to ship quality machine learning code.
The O’Reilly Podcast: Larry Haig discusses how to create a performance culture.
Effective communication combined with incremental adjustments to plans and practices can turn even the most challenging work situations around.
Five questions for Ben Sigelman about adopting distributed tracing tools and optimizing performance of complex, distributed systems.
The O’Reilly Security Podcast: Key preparation before implementing a vulnerability disclosure policy, the crucial role of setting scope, and the benefits of collaborative relationships.
Learn the techniques and strategies for progressively enhancing your tests so they adapt to the site you’re testing.
Five questions for Brendan Gregg about improving the performance of Linux systems.
A new role focused on creating data products and making data science work in production.
Approaches to data analysis, iterative workflows, and writing a book with Jupyter.
Beth Rattner on applying nature’s design solutions to human design challenges.
The O'Reilly Podcast: Ken Krupa on the challenge of data integration, and a solution.
Bridge the operational silos between web application architecture, system administration, and teams with DC/OS.
This two-day live online training covers JShell, a tool Paul Deitel calls “one of Java’s most significant new learning, discovery, and developer-productivity-enhancement features since its inception 20+ years ago.”
Considerations for moving your web app from a monolith to microservice architecture.
The O'Reilly Podcast: Andy Hickl on sources of bias in artificial intelligence—and how to address them.
Getting started with data science, Jupyter as shareable hub, and JupyterLab adoption.
The O’Reilly Programming Podcast: Applying open source practices beyond software.
Nothing says machine learning can't outperform humans, but it's important to realize perfect machine learning doesn't, and won't, exist.
Five questions for Lee Calcote on the scaling and performance of container architectures.
The benefits of employing an improvisational comedy tool in security communication.
Five questions for Emil Stolarsky on moving logic to the edge of your systems.
The tools of defensive computing, whether they involve mascara and face paint or random autonomous web browsing, belong to the harsh reality we've built.
Jupyter as a learning tool, the JupyterHub Project, and Music21.
Script generation from RNNs, Tensorflow book companion notebooks, transportation insights from notebooks, machine learning notebooks.
Sukiyaki in French style, brick-and-mortar conversion tracking, route-based pricing, and technological productivity.
How to use Apache Spark’s Resilient Distributed Dataset (RDD) API.
Ziya Ma outlines the challenges for applying machine learning and deep learning at scale and shares solutions that Intel has enabled for customers and partners.
Eddie Copeland explores how the London Office of Data Analytics overcame the barriers to joining, analyzing, and acting upon public sector data at city scale.
Grace Huang shares lessons learned from running and interpreting machine-learning experiments.
Tim O’Reilly delves into past technological transitions, speculates on the possibilities of AI, and looks at what's keeping us from making the right choices to govern our creations.
Aida Mehonic explores the role artificial intelligent might play in the financial world.
Tom Smith explains how the UK's Office of National Statistics is using data science to create repeatable, accurate, and transferable statistical research.
The O’Reilly Data Show Podcast: Jeremy Stanley on hiring and leading machine learning engineers to build world-class data products.
The O’Reilly Design Podcast: What makes healthy teams healthy, being customer obsessed, and design and research at Microsoft.
Project Jupyter co-founder Brian Granger on the JupyterLab project, its potential role in scientific and tech communities, and the expanding role of notebooks.
The O’Reilly Bots Podcast: The technical and social dynamics of solving scheduling problems.
Watch highlights covering data-driven business, data engineering, machine learning, and more. From Strata Data Conference in London 2017.
Using the music industry as an example, Paul Brook shows how modern information points bring new data that changes the way an organization will make decisions.
Anthony Goldbloom shares lessons learned from top performers in the Kaggle community and explores the types of machine-learning techniques typically used.
Miriam Redi investigates how machine learning can detect subjective properties of images and videos, such as beauty, creativity, and sentiment.
Darren Strange asks: What part will we each play in what is sure to be one of the most exciting times in computer science?
Aurélie Pols draws a broad philosophical picture of the data ecosystem and then hones in on the right to data portability.
M. C. Srivas covers Uber's big data architecture and explores the real-time problems Uber needs to solve to make ride sharing smooth.
Learn what it takes to make the most of your design team.
The O’Reilly Security Podcast: How adversarial posture affects decision-making, how decision trees can build more dynamic defenses, and the imperative role of UX in security.
Level up your skills set before diving into React.
Five questions for Gwen Shapira about how Kafka can enable business agility.
TensorFlow cookbook materials, source notebooks, Python lectures, and Software Carpentry.
AutoML, AI photo editing, AI product studio, and Apple and dark data.
Cindy Alvarez outlines the components of a hypothesis and shares examples of successful and unsuccessful hypotheses.
Rana el Kaliouby discusses the techniques, possibilities, and challenges around emotion AI today.