Curated articles, resources, tips and trends from the DevOps World.
After 20 years, and 3283 posts adding up to 1,577,106 words I am wrapping up my time as the lead blogger on the AWS News Blog.
AI continues to make inroads into companies of all sizes, types, and industries, with the enterprise tech industry being one of the most active in embracing AI. I’ve already spoken with developers about how they’re adopting AI tools and creatively using them in their own workflows.
This time last year, I wrote that AI engineering in 2023 was defined by a proliferation of LLMs and an expansion of AI dev tooling. In 2024, those trends continued — but also the market for both LLMs and AI development tools matured considerably.
The week after AWS re:Invent builds on the excitement and energy of the event and is a good time to learn more and understand how the recent announcements can help you solve your challenges. As usual, we have you covered with our top announcements of AWS re:Invent 2024 post.
Today we’re going to be talking about Strings in Java. If you write Java often, you know that a String is considered a first-class object, even though it is not one of the eight primitive types. What you may not know is how to handle Strings in production applications best.
This ongoing Docker Labs GenAI series explores the exciting space of AI developer tools. At Docker, we believe there is a vast scope to explore, openly and without the hype. We will share our explorations and collaborate with the developer community in real time.
Imagine boosting developer productivity by 30% while slashing project costs by 25%. That might sound impossible, yet it’s a realistic goal for organizations that adopt FinOps (financial operations), a data-driven approach to measuring software development life-cycle (SDLC) costs.
Discover Kubernetes' strict CPU manager for enhanced performance and reliability in resource allocation for workloads.
The future of enterprise-grade AI is facing a massive challenge: data. An AI model is only as good as the data it is trained on. Building models that yield meaningful results require enormous volumes of high-quality data for training and refinement.
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