Where Programming, Ops, AI, and the Cloud are Headed in 2021
Following O'Reilly online learning trends to see what's coming next.
Analysis of notable trends based on original surveys, usage, and data sets.
Following O'Reilly online learning trends to see what's coming next.
Everyone’s talking about microservices. Who’s actually doing it?
O’Reilly usage analysis shows continued growth in AI/ML and early signs that organizations are experimenting with advanced tools and methods.
O’Reilly survey results show that AI efforts are maturing from prototype to production, but company support and an AI/ML skills gap remain obstacles.
Our annual analysis of the O’Reilly online learning platform reveals Python’s continued dominance and important shifts in infrastructure, AI/ML, cloud, and security.
O’Reilly survey highlights the increasing attention organizations are giving to data quality and how AI both exacerbates and alleviates data quality issues.
O’Reilly survey results indicate demand for serverless will grow in the near term as a worthwhile infrastructure option for many organizations.
Cloud native, security, performance, and SRE are areas of emphasis for the O’Reilly Velocity Conference in Berlin.
Machine learning, artificial intelligence, data engineering, and architecture are driving the data space.
To successfully implement AI technologies, companies need to take a holistic approach toward retraining their workforces.
Speech adds another level of complexity to AI applications—today’s voice applications provide a very early glimpse of what is to come.
A look at how guidelines from regulated industries can help shape your ML strategy.
Roger Magoulas explains how O’Reilly’s Radar methodology identifies emerging tech trends businesses need to know.
To successfully integrate AI and machine learning technologies, companies need to take a more holistic approach toward training their workforce.
We now are in the implementation phase for AI technologies.
Cloud native, AI/ML, and data tools and topics are areas of emphasis for the O’Reilly Open Source Software Conference.
Companies successfully adopt machine learning either by building on existing data products and services, or by modernizing existing models and algorithms.