The trinity of errors in applying confidence intervals: An exploration using Statsmodels
Three reasons why confidence intervals should not be used in financial data analyses.
Ideas
What's on our radar.
New live online training courses
Get hands-on training in machine learning, cybersecurity, conflict resolution, Python, microservices, and many other topics.
Four Short Links has moved
New editions of Four Short Links are available at oreilly.com/radar.
O'Reilly acquires Katacoda—and a new way for 2.5M customers to learn
Katacoda’s interactive learning scenarios and sandboxes will help O’Reilly users master important technologies.
There’s a straight line between strong decision-making and good software architecture
Thoughtful and effective decision-making was a key trend at the O’Reilly Software Architecture Conference in Berlin 2019.
Introducing the O’Reilly Infrastructure & Ops Conference
Every business that relies on technology is facing an infrastructure upheaval.
Highlights from the O'Reilly Software Architecture Conference in Berlin 2019
Experts explore new trends, tools, and techniques in software architecture.
Highlights from the O'Reilly Velocity Conference in Berlin 2019
Experts explore new trends, tools, and techniques in systems engineering and cloud native systems.
People, systems, and the California fires
Watching the O'Reilly teams accept the challenge of the Kincade fire and subsequent power outages was nothing short of remarkable.
Highlights from TensorFlow World in Santa Clara, California 2019
Experts explore TensorFlow 2.0's machine learning capabilities as well as the broader tools and applications of TensorFlow.
Highlights from the O’Reilly Artificial Intelligence Conference in London 2019
Experts explore AI's most promising developments, emerging technologies, and profitable use cases.
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.
Java and the Java Virtual Machine for cloud native applications
Java is ready for the cloud today.
Checking in on TensorFlow 2.0: Keras, API cleanup, and more
Paige Bailey, TensorFlow product manager at Google, highlights notable features of TensorFlow 2.0 and looks ahead to near-term updates.
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.
Highlights from the Strata Data Conference in New York 2019
Experts explore new trends, tools, and techniques in data and machine learning.
2019 Emerging AI Pioneers Showcase winners
AI startups vied for awards at the O’Reilly Artificial Intelligence Conference in San Jose.
Highlights from the O’Reilly Artificial Intelligence Conference in San Jose 2019
Experts discuss new trends, tools, and issues in artificial intelligence and machine learning.
Understanding deep neural networks
The O’Reilly Data Show Podcast: Michael Mahoney on developing a practical theory for deep learning.
Topics to watch at the Strata Data Conference in New York 2019
Machine learning, artificial intelligence, data engineering, and architecture are driving the data space.
Kubernetes: A simple overview
Get a basic understanding of Kubernetes and then go deeper with recommended resources.
How graph algorithms improve machine learning
A look at why graphs improve predictions and how to create a workflow to use them with existing machine learning tasks.
How new tools in data and AI are being used in health care and medicine
An overview of applications of new tools for overcoming silos, and for creating and sharing high-quality data.
Becoming a machine learning practitioner
The O’Reilly Data Show Podcast: Kesha Williams on how she added machine learning to her software developer toolkit.
One simple chart: Who is interested in Apache Pulsar?
Multi-layer architecture, scalability, multitenancy, and durability are just some of the reasons companies have been using Pulsar.
How organizations are sharpening their skills to better understand and use AI
To successfully implement AI technologies, companies need to take a holistic approach toward retraining their workforces.
Antitrust regulators are using the wrong tools to break up Big Tech
What we really need is disclosure of information about the growth and health of the supply side of Big Tech's marketplaces.
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.
Blockchain solutions in enterprise
A review of the crucial steps for a successful blockchain-based solution.
Got speech? These guidelines will help you get started building voice applications
Speech adds another level of complexity to AI applications—today’s voice applications provide a very early glimpse of what is to come.
Make data science more useful
The O’Reilly Data Show Podcast: Cassie Kozyrkov on connecting data and AI to business.
Taming chaos: Preparing for your next incident
Tim Craig and Gustavo Franco on establishing robust and well-supported incident response processes.
Learning from adversaries
Adversarial images aren’t a problem—they’re an opportunity to explore new ways of interacting with AI.
One simple graphic: Researchers love PyTorch and TensorFlow
Interest in PyTorch among researchers is growing rapidly.
Video
The war for the soul of open source
Drawing on 13 years spent building the Chef community, Adam Jacob takes a deep dive into the soul of open source.
Video
Open source force multipliers
Adrian Cockcroft says the most successful open-source-based businesses have turned their partners and developer communities into force multipliers for their own marketing and engineering teams.
Video
Managing machines
Pete Skomoroch covers what you need to know as we shift from a world of deterministic programs to systems that give unpredictable results on ever-changing training data.
Video
O’Reilly Radar: Open source technology trends—What our users tell us
Using aggregate analysis of O’Reilly online learning content usage and search data, Roger Magoulas shares key insights that impact the technology tools ecosystem.
Video
O'Reilly Open Source and Frank Willison Awards
The O’Reilly Open Source Awards recognize individual contributors who have demonstrated exceptional leadership, creativity, and collaboration in the development of open source software.
Video
Ask not what Brands™ can do for you
VM Brasseur discusses the help new companies need to become authentic members of the free and open source software community.
Acquiring and sharing high-quality data
The O’Reilly Data Show Podcast: Roger Chen on the fair value and decentralized governance of data.
Video
The next age of open innovation
Alison McCauley looks at how blockchain technology offers new tools that can help extend the ethos of open innovation into new areas.
Video
Better living through software
Tiffani Bell shares three lessons she's learned exploring how technology can help the less fortunate.
Video
Why Amazon cares about open source
Arun Gupta discusses the reasons why AWS is committed to open projects and communities.
Video
The role of open source in mitigating natural disasters
Pedro Cruz and Brad Topol discuss Call for Code, a global developer competition that uses open source technologies to address natural disasters.
Highlights from the O'Reilly Open Source Software Conference in Portland 2019
Experts explore the role open source software plays in fields as varied as machine learning, blockchain, disaster response, and more.
Video
Built to last: Building and growing open source communities
Kay Williams explores key lessons for building strong open source communities based on Microsoft’s real-world experience with Kubernetes and VSCode.
Managing machine learning in the enterprise: Lessons from banking and health care
A look at how guidelines from regulated industries can help shape your ML strategy.
The circle of fairness
We shouldn't ask our AI tools to be fair; instead, we should ask them to be less unfair and be willing to iterate until we see improvement.
Highlights from the O'Reilly Artificial Intelligence Conference in Beijing 2019
Experts explore the future of hiring, AI breakthroughs, embedded machine learning, and more.
Video
The future of machine learning is tiny
Pete Warden digs into why embedded machine learning is so important, how to implement it on existing chips, and shares new use cases it will unlock.
Video
Data orchestration for AI, big data, and cloud
Haoyuan Li offers an overview of a data orchestration layer that provides a unified data access and caching layer for single cloud, hybrid, and multicloud deployments.
Video
AI and retail
Mikio Braun takes a look at Zalando and the retail industry to explore how AI is redefining the way ecommerce sites interact with customers.
Video
AI and systems at RISELab
Ion Stoica outlines a few projects at the intersection of AI and systems that UC Berkeley's RISELab is developing.
Video
Designing computer hardware for artificial intelligence
Michael James examines the fundamental drivers of computer technology and surveys the landscape of AI hardware solutions.
Video
The future of hiring and the talent market with AI
Maria Zheng examines AI and its impact on people’s jobs, quality of work, and overall business outcomes.
Video
Top AI breakthroughs you need to know
Abigail Hing Wen discusses some of the most exciting recent breakthroughs in AI and robotics.
Video
Toward learned algorithms, data structures, and systems
Tim Kraska outlines ways to build learned algorithms and data structures to achieve “instance optimality” and unprecedented performance for a wide range of applications.
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.
RISELab’s AutoPandas hints at automation tech that will change the nature of software development
Neural-backed generators are a promising step toward practical program synthesis.
One simple chart: Who is interested in Spark NLP?
As we close in on its two-year anniversary, Spark NLP is proving itself a viable option for enterprise use.
AI and machine learning will require retraining your entire organization
To successfully integrate AI and machine learning technologies, companies need to take a more holistic approach toward training their workforce.
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.