Our entire economy seems to have forgotten that workers are also consumers, and suppliers are also customers.
Organizations that want all of the speed, agility, and savings the cloud provides are embracing a cloud native approach.
Embedded Computer Vision, Unix History, Unionizing Workforce, and Text Adventure AI
Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors.
The O’Reilly Data Show Podcast: Kartik Hosanagar on the growing power and sophistication of algorithms.
The internet itself is a changing context—we’re right to worry about data flows, but we also have to worry about the context changing even when data doesn’t flow.
Radar spots and explores emerging technology themes so organizations can succeed amid constant change.
Mapping the complex forces that are reshaping organizations and changing the employee/employer relationship.
At O’Reilly, we seek to foster a culture that creates opportunity, rewards and recognizes accomplishments, and treats everyone with respect.
NLP systems in health care are hard—they require broad general and medical knowledge, must handle a large variety of inputs, and need to understand context.
Get hands-on training in machine learning, AWS, Kubernetes, Python, Java, and many other topics.
The O’Reilly Data Show Podcast: P.W. Singer on how social media has changed, war, politics, and business.
The software industry has demonstrated, all too clearly, what happens when you don’t pay attention to security.
The most promising area in the application of deep learning methods to time series forecasting is in the use of CNNs, LSTMs, and hybrid models.
There are growing numbers of users and contributors to the framework, as well as libraries for reinforcement learning, AutoML, and data science.
An overview of emerging trends, known hurdles, and best practices in artificial intelligence.
The O’Reilly Data Show Podcast: Siwei Lyu on machine learning for digital media forensics and image synthesis.
To meet the challenge of producing more food with less everything, farm bots are going to be an essential part of the mix.
Highlights and use cases from companies that are building the technologies needed to sustain their use of analytics and machine learning.
Glenn Vanderburg talks about the importance of letting your attention roam, and he shares examples of how insights from other fields have inspired software practitioners.
Matt Stine looks at the tricky situations that sometimes emerge from design and architecture.
Stuart Halloway explains how to augment agility with principles for designing systems.
Trisha Gee shares lessons she learned the hard way while managing her career as a developer, lead, and technical advocate.
Watch highlights from expert talks covering cloud-native programming, software architecture career advice, and more.
Neal Ford talks with Mark Richards about his career path and his work as a software architect.
Tamar Eilam offers an overview of cloud-native programming and outlines a path toward the unification of the cloud programming model.
Analysis of the O’Reilly online learning platform reveals a new approach to technical architecture, the rise of blockchain, and shifts in programming language adoption.
How companies in Europe are preparing for and adopting AI and ML technologies.
Much like human speech, bird song learning is social; perhaps we'll discover machine learning is social, too.
The O’Reilly Data Show Podcast: Maryam Jahanshahi on building tools to help improve efficiency and fairness in how companies recruit.
A recent survey investigated how companies are approaching their AI and ML practices, and measured the sophistication of their efforts.
Consent is the first step toward the ethical use of data, but it's not the last.
Experts weigh in on GraphQL, machine learning, React, micro-frontends, and other trends that will shape web development.
An exploration of three types of errors inherent in all financial models.
From artificial intelligence to serverless to Kubernetes, here’s what's on our radar.
The O’Reilly Data Show Podcast: Andrew Burt on the need to modernize data protection tools and strategies.
O’Reilly authors and instructors explore the near-term future of popular and growing programming languages.
Survey results indicate incident response times improve with AI-based security services.
The program for our Artificial Intelligence Conference in New York City will showcase tools, best practices, and use cases from companies leading the way in AI adoption.
How new developments in automation, machine deception, hardware, and more will shape AI.
Technological change often happens gradually, then suddenly. Tim O'Reilly explores the areas poised for sudden shifts.
From infrastructure to tools to training, Ben Lorica looks at what’s ahead for data.
The O’Reilly Data Show Podcast: Haoyuan Li on accelerating analytic workloads, and innovation in data and AI in China.
The O’Reilly Data Show Podcast: Ben Lorica looks ahead at what we can expect in 2019 in the big data landscape.
An overview of NAS and a discussion on how it compares to hyperparameter optimization.
We need to do more than automate model building with autoML; we need to automate tasks at every stage of the data pipeline.
Our most-used AWS resources will help you stay on track in your journey to learn and apply AWS.
When it comes to automation of existing tasks and workflows, you need not adopt an “all or nothing” attitude.
The O’Reilly Data Show Podcast: Alex Wong on building human-in-the-loop automation solutions for enterprise machine learning.
Get a basic understanding of distributed systems and then go deeper with recommended resources.
A new report explores how far companies have come with microservices.
The O’Reilly Data Show Podcast: Vitaly Gordon on the rise of automation tools in data science.
Our most-used Java resources will help you stay on track in your journey to learn and apply Java.
Considerations for a world where ML models are becoming mission critical.
The O’Reilly Data Show Podcast: Francesca Lazzeri and Jaya Mathew on digital transformation, culture and organization, and the team data science process.
Understanding how the Kubernetes scheduler makes scheduling decisions is critical to ensure consistent performance and optimal resource utilization.
Jane Adams examines the ways data-driven recruiting fails to achieve intended results and perpetuates discriminatory hiring practices.
Kris Nova looks at the new era of the cloud native space and the kernel that has made it all possible: Kubernetes.
Claire Janisch looks at some of the best biomimicry opportunities inspired by nature’s software and wetware.
Martin Kleppmann shows how recent computer science research is helping develop the abstractions and APIs for the next generation of applications.
Omoju Miller outlines a vision where we harness human action for a better future.
Crystal Hirschorn discusses how organizations can benefit from combining established tech practices with incident planning, post-mortem-driven development, chaos engineering, and observability.
Anne Currie says excessive and dirty energy use in data centers is one of the biggest ethical issues facing the tech industry.