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.
Content Moderation, Robust Learning, Archiving Floppies, and xkcd Charting
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.
Get hands-on training in deep learning, AI applications, business strategies, Python, data analysis, and many other topics.
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.
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.
The O’Reilly Open Source Awards recognize individual contributors who have demonstrated exceptional leadership, creativity, and collaboration in the development of open source software.
Drawing on 13 years spent building the Chef community, Adam Jacob takes a deep dive into the soul of open source.
VM Brasseur discusses the help new companies need to become authentic members of the free and open source software community.
Using aggregate analysis of O’Reilly online learning content usage and search data, Roger Magoulas shares key insights that impact the technology tools ecosystem.
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.
The O’Reilly Data Show Podcast: Roger Chen on the fair value and decentralized governance of data.
Tiffani Bell shares three lessons she's learned exploring how technology can help the less fortunate.
Experts explore the role open source software plays in fields as varied as machine learning, blockchain, disaster response, and more.
Arun Gupta discusses the reasons why AWS is committed to open projects and communities.
Alison McCauley looks at how blockchain technology offers new tools that can help extend the ethos of open innovation into new areas.
Kay Williams explores key lessons for building strong open source communities based on Microsoft’s real-world experience with Kubernetes and VSCode.
Pedro Cruz and Brad Topol discuss Call for Code, a global developer competition that uses open source technologies to address natural disasters.
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.
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.
Tim Kraska outlines ways to build learned algorithms and data structures to achieve “instance optimality” and unprecedented performance for a wide range of applications.
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.
Maria Zheng examines AI and its impact on people’s jobs, quality of work, and overall business outcomes.
Michael James examines the fundamental drivers of computer technology and surveys the landscape of AI hardware solutions.
Abigail Hing Wen discusses some of the most exciting recent breakthroughs in AI and robotics.
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.
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.
A look at the landscape of tools for building and deploying robust, production-ready machine learning models.
Machine learning solutions for data integration, cleaning, and data generation are beginning to emerge.
Michael Feathers explores various scaling strategies in light of research about human cognition and systems cohesion.
Rebecca Parsons shares the story of her career path and her work as an architect.
Adam Tornhill offers a new perspective on software development that will change how you view code.
Drawing from technology, finance, sports, social psychology, and complexity theory, Everett Harper looks at the key practices that are crucial for solving our most critical challenges.
Bob Quillin outlines how the cloud native community can reduce complexity, be more inclusive to all teams, and create a more open, multicloud future.
Drawing inspiration from restorative justice practices and her own journey of healing, Alex Qin offers a hopeful vision for how we can come together and co-create the world we yearn for.
Liz Fong-Jones says management of complex distributed systems requires changing who's involved in production, how they collaborate, and how success is measured.
Bridget Kromhout looks over the cloud native landscape and talks about what’s new, what’s next, and what you need to get started with Kubernetes right now.
Experts explore software architecture security, design heuristics, Next Architecture, and more.
Chris Guzikowski discusses the convergence of microservices, cloud, containers, and orchestration that points toward the rise of a Next Architecture.
Nathaniel Schutta explains why an architect’s job is to be a storyteller.
Michael Carducci takes an entertaining look at why humans are so easy to fool, and he explores what we can do to overcome our weaknesses and build more secure software.
Rebecca Wirfs-Brock explores how you can grow as a designer by becoming conscious of your heuristics.
Jessica Kerr argues that most programming careers aren’t about writing software, they’re about changing it.
Chen Goldberg shares how Kubernetes, Istio, GKE, and Anthos can help build distributed systems and happy teams.
Experts explore cloud native infrastructure, SRE, distributed systems, and more.
Lachlan Evenson and Bridget Kromhout discuss the journey to build Gatekeeper, a community-driven approach for enforcing policy on any Kubernetes cluster.
Yaniv Aknin dives into the secret sauce for a successful SRE organization: high-quality measurements of reliability.
Modern distributed systems are immensely different from distributed systems of just a decade ago. Lena Hall looks at how our approaches and practices progress with time.
We now are in the implementation phase for AI technologies.
Google SRE Stephen Thorne shares best practices for starting an SRE team at your company.
We won’t get the chance to worry about artificial general intelligence if we don’t deal with the problems we have in the present.