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.
Ethics and OKRs, Rewriting Binaries, Diversity of Implementation, and Uber's Metrics Systems
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.
Adam Tornhill offers a new perspective on software development that will change how you view code.
Rebecca Parsons shares the story of her career path and her work as an architect.
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.
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.
Liz Fong-Jones says management of complex distributed systems requires changing who's involved in production, how they collaborate, and how success is measured.
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.
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.
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.
Chen Goldberg shares how Kubernetes, Istio, GKE, and Anthos can help build distributed systems and happy teams.
Jessica Kerr argues that most programming careers aren’t about writing software, they’re about changing it.
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.
The O’Reilly Data Show Podcast: Dhruba Borthakur and Shruti Bhat on enabling interactive analytics and data applications against live data.
Get hands-on training in Docker, microservices, cloud native, Python, machine learning, and many other topics.
Cloud native, AI/ML, and data tools and topics are areas of emphasis for the O’Reilly Open Source Software Conference.
How SREs can use a hierarchy for mature alerts.
The O’Reilly Data Show Podcast: Jike Chong on the many exciting opportunities for data professionals in the U.S. and China.
Companies successfully adopt machine learning either by building on existing data products and services, or by modernizing existing models and algorithms.
Microservices, serverless, AI, ML, and Kubernetes are among the most notable topics in our analysis of proposals from the O’Reilly Software Architecture Conference.
From data quality to personalization, to customer acquisition and retention, and beyond, AI and ML will shape the customer experience of the future.
Breaking up Facebook won't solve the disinformation or privacy problems. It might well make it harder for Facebook to work on those problems.
Programmers have built great tools for others. It’s time they built some for themselves.
The O’Reilly Data Show Podcast: Jeff Jonas on the evolution of entity resolution technologies.
Sandra Wachter argues that a right to reasonable inferences could protect against new forms of discrimination.
Shingai Manjengwa shares insights from teaching data science to 300,000 online learners.
Chris Taggart explains the benefits of “white box data” and outlines the structural shifts that are moving the data world toward this model.
David Boyle shares lessons on how analysts can harness data and creativity to build partnerships.
Mike Tidmarsh looks at how data and AI are radically reshaping the world of marketing communications.
Watch highlights from expert talks covering machine learning, predictive analytics, data regulation, and more.
Mick Hollison describes why hybrid and multi-cloud is the future for organizations that want to capitalize on machine learning and AI.
Drawing insights from recent surveys, Ben Lorica analyzes important trends in machine learning.
Cassie Kozyrkov explains how organizations can extract more value from their data.
James Burke asks if we can use data and predictive analytics to take the guesswork out of prediction.
Cait O’Riordan discusses the North Star metric the Financial Times uses across the organization to drive subscriber growth.
More than anything else, O'Reilly's AI Conference was about making the leap to AI 2.0.
Survey results reveal the path organizations face as they integrate cloud native infrastructure and harness the full power of the cloud.
The O’Reilly Data Show Podcast: Neelesh Salian on data lineage, data governance, and evolving data platforms.
Resolving the volatility problem will unlock the groundwork needed for blockchain-based global payment systems.
Sean Gourley considers the repercussions of AI-generated content that blurs the line between what's real and what's fake.
Kim Hazelwood discusses the hardware and software Facebook has designed to meet its scale needs.
How can machine learning decode the mysteries of life? Olga Troyanskaya explores this and other big questions through the prism of deep learning.
Nick Curcuru explains how Mastercard is using AI to improve security without sacrificing the customer experience.
Rajendra Prasad explains how leaders in large enterprises can make AI adoption successful.
Christopher Ré discusses Snorkel, a system for fast training data creation.
Carlos Humberto Morales offers an overview of Nauta, an open source multiuser platform that lets data scientists run complex deep learning models on shared hardware.
Ruchir Puri discusses the next revolution in automating AI, which strives to deploy AI to automate the task of building, deploying, and managing AI tasks.
Kurt Muehmel explores AI within a broader discussion of the ethics of technology, arguing that inclusivity and collaboration are necessary.
Watch highlights from expert talks covering AI, machine learning, deep learning, ethics, and more.