Curated articles, resources, tips and trends from the DevOps World.
APIs are a cornerstone of the modern internet, serving an endless variety of use cases and making it possible to integrate and communicate between systems and applications. A large subset of these APIs is what we call “data APIs”: APIs that enable access to a database through structured queries.
StormForge sponsored this post. StormForge is under common control with TNS. As organizations increasingly adopt Kubernetes for their infrastructure, understanding and optimizing its performance becomes vital.
With the rise of generative AI, the top hyperscalers — Amazon Web Services, Google, and Microsoft — are engaging in yet another round of intense competitive battles. Generative AI needs massive computing power and large datasets, which makes the public cloud an ideal platform choice.
This is the second in a five-part series. Read Part 1 here. In this series we learned what eBPF is, the tools to work with it, why eBPF performance is important and how to track it with continuous benchmarking.
When you think about artificial intelligence (AI) and machine learning (ML), the OpenStack Infrastructure as a Service (IaaS) cloud, and its object storage component, Swift is not the first technology to come to mind.
We’re fond of saying that there’s no artificial intelligence without data. But it can’t be any kind of data. Take large language models, or LLMs — deep learning models, like OpenAI’s GPT-4 that can generate text that’s quite similar to what a human would write.
Organizations were caught off guard by large language models, and they’re playing catch up, according to responsible AI ethicists. Now that the proverbial cat is out of the bag, and pending outright bans, it’s time to get some footing on AI ethics.
Everyone wants more security for their cloud processes and data. So, to deliver this, Microsoft announced at the recent OpenInfra Summit that it’s closer to delivering it to its Azure customers.
The five steps to deploy cloud native sustainable foundation AI models starts with the obvious two: containers to manage the workloads and Kubernetes to deploy across a distributed infrastructure.
Data networks are generally used for file sharing, application operations or internet access, but what about a network strictly for distributing application programming interfaces? After all, an API is pretty esoteric, given that it is not standard data but a set of rules that define how two pieces
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