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Developing AI and machine learning applications requires plenty of GPUs. Should you run them on-premises or in the cloud?
AI coding tools such as Amazon CodeWhisperer and GitHub Copilot boost the productivity of pro and novice developers alike. They’re only the beginning.
Generative AI systems for business are alarmingly inaccurate. Data needs some serious attention to avoid wrong info, bias, or legal trouble.
Cloud providers are becoming commoditized, so you have to be careful to determine where the best value lies.
AI-enabled deploy assist in Netlify’s Composable Web Platform diagnoses deployment failures and build errors and suggests fixes.
Everyone seems to agree that Kubernetes is too expensive to run. The problem is the way we build applications.
Firewall for AI will analyze user prompts to large language models to identify attempts to extract data or otherwise exploit a model, Cloudflare said.
Let’s clear up the confusion around the semantics of these critical roles. They offer a combo of strategic vision and on-the-ground development skills.
Software freedom. User freedom. Why do we have to choose? Licensing hasn’t kept pace with the reality of software in the cloud and AI.
Cloud can be a green technology, but not without significant planning and up-front work that most enterprises are reluctant to fund.