Understanding gRPC in the dawn of microservices.
A deep dive into Uber's engineering effort to optimize geospatial queries in Presto.
BMI, Govt Apps Threatened, Geospatial Jupyter, and W3C Adds DRM to HTML (*spit*).
Building convnets from scratch with TensorFlow and TensorBoard.
The O'Reilly Podcast: Dave Cassel on building a unified enterprise database to store and query any type of data.
Learn about some of the common issues you will encounter when developing algorithms for a modern anomaly detection system.
Learn the difference between live and streaming anomaly detection systems and how to address the challenges different data velocities pose.
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.
A deep dive into startup TuSimple’s use of Apache MXNet.
The O’Reilly Security Podcast: Shifting secure code responsibility to developers, building secure software quickly, and the importance of changing processes.
Exciting new genetic testing technology has improved the speed and accuracy of cancer diagnosis.
Data capture, management, and analysis builds a bridge between design, user experience, and business relevance.
Learn some of the benefits of using real-time processing of data for some use cases.
Learn to identify problems that may indicate data team dysfunction.
Learn about the four major image and object formats accepted by Figma - and three ways to import them.
Learn about Figma's layers area – where you control individual objects, images, and text – and avoid making overly complex Figma documents.
Learn about Figma's Frame tool — then use it to define the correct workspace for the prototype of your website, tablet app, or smart phone app.
6 lessons learned to get a quick start on productivity.
A look at the Layer API, TFLearn, and Keras.
Artificial intelligence is emerging as a creative force; in the process, it reveals something of itself.
Turning abstract AI into real business solutions.
Sound design should not be an afterthought at the end of a design process.
The O’Reilly Programming Podcast: A look at what’s new in Java 9 and Spring 5.
Learn how to set up an external job within Jenkins and manage its execution using the Jenkins command line interface.
Learn how to automate Jenkins continuous integration projects with Apache Ant, a popular build tool for developing software.
Applications of CNNs for real-time image classification in the enterprise.
Building a production-grade real-time image classification system.
Learn to install Ant software - and get it ready to integrate with Jenkins - by using the Jenkins automatic installation feature.
Are we out of the woods?
Five questions for Bryan Liles on the complexities of tracing, recommended tools and skills, and how to learn more about monitoring.
Generate new images and fix old ones using neural networks.
Why machine learning needs real-time data infrastructure.
Learn how to use IBM Watson's APIs and natural language understanding to analyze the tone of social media posts like tweets.
Learn how to use IBM Watson's APIs and natural language understanding to extract information about people and companies from news articles.
Steve Portigal shares what can go wrong in the real world.
The O’Reilly Data Show Podcast: Kenneth Stanley on neuroevolution and other principled ways of exploring the world without an objective.
There’s beauty in biology—and the biotech industry is ready to make a move
Understanding the impact and expanding influence of DevOps culture, and how to apply DevOps principles to make your digital operations more performant and productive.
Help PMs navigate the challenges that will test them as they learn about and navigate your organization.
The O’Reilly Security Podcast: The open-ended nature of incident response, and how threat intelligence and incident response are two pieces of one process.
Building and tuning traffic management for large web-scale applications.
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.
Brett Cannon looks at how healthy expectations can maintain a balanced relationship between open source users and project maintainers.
Nadia Eghbal explores how money can support open source development without changing its incentives.
William Merchan shares fundamental trends driving the adoption of Jupyter and its deployment in large organizations.
Jeremy Freeman describes a growing ecosystem of scientific solutions, many of which involve Jupyter.
Five questions for Charles Givre on building effective security analytics programs.
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.
Fernando Perez explains how Project Jupyter fits into a vision of collaborative development of tools that are applicable to research, education, and industry.
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.
Rachel Thomas shares her experience using Jupyter notebooks to help students understand deep learning through experimentation.
The O’Reilly Programming Podcast: Building applications that work everywhere for everyone.
Martin Charlier explores a new technique to apply in your prototyping work.
Recent trends in practical use and a discussion of key bottlenecks in supervised machine learning.
Five questions for Brendan Burns: How containers and cluster management have changed systems development, and common patterns for building distributed systems.
The toughest part of machine learning with Spark isn't what you think it is.
Shreya Thiagarajan describes the invaluable experience she gained while working in a local community lab.
Machine learning opens the door for the use of training rather than programming in game development.
A look at real-world instances where design collaboration has achieved excellent results.