Three reasons why confidence intervals should not be used in financial data analyses.
Katacoda’s interactive learning scenarios and sandboxes will help O’Reilly users master important technologies.
Thoughtful and effective decision-making was a key trend at the O’Reilly Software Architecture Conference in Berlin 2019.
Every business that relies on technology is facing an infrastructure upheaval.
Experts explore new trends, tools, and techniques in software architecture.
Experts explore new trends, tools, and techniques in systems engineering and cloud native systems.
Watching the O'Reilly teams accept the challenge of the Kincade fire and subsequent power outages was nothing short of remarkable.
Experts explore TensorFlow 2.0's machine learning capabilities as well as the broader tools and applications of TensorFlow.
Experts explore AI's most promising developments, emerging technologies, and profitable use cases.
The O’Reilly Data Show Podcast: Peter Bailis on data management, ML benchmarks, and building next-gen tools for analysts.
Java is ready for the cloud today.
Paige Bailey, TensorFlow product manager at Google, highlights notable features of TensorFlow 2.0 and looks ahead to near-term updates.
The O’Reilly Data Show Podcast: Arun Kejariwal and Ira Cohen on building large-scale, real-time solutions for anomaly detection and forecasting.
Experts explore new trends, tools, and techniques in data and machine learning.
AI startups vied for awards at the O’Reilly Artificial Intelligence Conference in San Jose.
Experts discuss new trends, tools, and issues in artificial intelligence and machine learning.
The O’Reilly Data Show Podcast: Michael Mahoney on developing a practical theory for deep learning.
Machine learning, artificial intelligence, data engineering, and architecture are driving the data space.
Get a basic understanding of Kubernetes and then go deeper with recommended resources.
A look at why graphs improve predictions and how to create a workflow to use them with existing machine learning tasks.
An overview of applications of new tools for overcoming silos, and for creating and sharing high-quality data.
The O’Reilly Data Show Podcast: Kesha Williams on how she added machine learning to her software developer toolkit.
Multi-layer architecture, scalability, multitenancy, and durability are just some of the reasons companies have been using Pulsar.
To successfully implement AI technologies, companies need to take a holistic approach toward retraining their workforces.
What we really need is disclosure of information about the growth and health of the supply side of Big Tech's marketplaces.
The O’Reilly Data Show Podcast: Alex Ratner on how to build and manage training data with Snorkel.
A review of the crucial steps for a successful blockchain-based solution.
Speech adds another level of complexity to AI applications—today’s voice applications provide a very early glimpse of what is to come.
The O’Reilly Data Show Podcast: Cassie Kozyrkov on connecting data and AI to business.
Tim Craig and Gustavo Franco on establishing robust and well-supported incident response processes.
Adversarial images aren’t a problem—they’re an opportunity to explore new ways of interacting with AI.
Interest in PyTorch among researchers is growing rapidly.
Drawing on 13 years spent building the Chef community, Adam Jacob takes a deep dive into the soul of open source.
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.
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.
Using aggregate analysis of O’Reilly online learning content usage and search data, Roger Magoulas shares key insights that impact the technology tools ecosystem.
The O’Reilly Open Source Awards recognize individual contributors who have demonstrated exceptional leadership, creativity, and collaboration in the development of open source software.
VM Brasseur discusses the help new companies need to become authentic members of the free and open source software community.
The O’Reilly Data Show Podcast: Roger Chen on the fair value and decentralized governance of data.
Alison McCauley looks at how blockchain technology offers new tools that can help extend the ethos of open innovation into new areas.
Tiffani Bell shares three lessons she's learned exploring how technology can help the less fortunate.
Arun Gupta discusses the reasons why AWS is committed to open projects and communities.
Pedro Cruz and Brad Topol discuss Call for Code, a global developer competition that uses open source technologies to address natural disasters.
Experts explore the role open source software plays in fields as varied as machine learning, blockchain, disaster response, and more.
Kay Williams explores key lessons for building strong open source communities based on Microsoft’s real-world experience with Kubernetes and VSCode.
A look at how guidelines from regulated industries can help shape your ML strategy.
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.
Experts explore the future of hiring, AI breakthroughs, embedded machine learning, and more.
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.
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.
Mikio Braun takes a look at Zalando and the retail industry to explore how AI is redefining the way ecommerce sites interact with customers.
Ion Stoica outlines a few projects at the intersection of AI and systems that UC Berkeley's RISELab is developing.
Michael James examines the fundamental drivers of computer technology and surveys the landscape of AI hardware solutions.
Maria Zheng examines AI and its impact on people’s jobs, quality of work, and overall business outcomes.
Abigail Hing Wen discusses some of the most exciting recent breakthroughs in AI and robotics.
Tim Kraska outlines ways to build learned algorithms and data structures to achieve “instance optimality” and unprecedented performance for a wide range of applications.
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.
Neural-backed generators are a promising step toward practical program synthesis.
As we close in on its two-year anniversary, Spark NLP is proving itself a viable option for enterprise use.
To successfully integrate AI and machine learning technologies, companies need to take a more holistic approach toward training their workforce.
The O’Reilly Data Show Podcast: Nick Pentreath on overcoming challenges in productionizing machine learning models.