Ever since generative AI burst into public consciousness, business executives and IT leaders have been intrigued by the potential of generative AI to transform IT. As it turns out, generative AI has already begun revolutionizing ITOps and we are excited to be at the forefront of this game-changing technology through the ScienceLogic SL1 Hollywood release.

Cisco Full-Stack Observability is a strategic investment that outshines Dynatrace by incorporating AI-driven insights, full-stack visibility, and a future-proof design that adapts to your evolving business needs. Application experience is the heart of your digital business, so choosing the right observability platform is not just a technical decision — it’s strategic.

This blog post explores the technical and strategic benefits of deploying open-source AI models on Ubuntu. We’ll highlight why it makes sense to use Ubuntu with open-source AI models, and outline the deployment process on Azure.

From its initial appearance in the dev-tools space, GenAI has had an outsized impact on how developers approach day-to-day tasks (just ask any developer about when they first started using GitHub’s copilot). While any risks are still being evaluated—like potential for introducing anti-patterns or inadvertently running afoul of compliance requirements, many engineering teams have successfully implemented GenAI with measurable gains in collaboration and productivity.

In today’s digital landscape, delivering a flawless customer experience is the ultimate competitive advantage. However, traditional methods of ensuring service resilience during operation can often be both expensive and cumbersome to maintain. This is where left-shift operations come into play—a powerful strategy aimed at instilling quality and resiliency in the early stages of building and delivering high-quality products and services..

In a previous blog post, which was a glossary of terms related to artificial intelligence, I included this brief definition of "feature extraction": Let’s go a bit deeper on that. In the ever-expanding landscape of machine learning, feature extraction stands out as a crucial technique for enhancing the performance of models and uncovering valuable insights from complex datasets.

In our previous post, we discussed how to generate Images using Stable Diffusion on AWS. In this post, we will guide you through running LLMs for text generation in your own environment with a GPU-based instance in simple steps, empowering you to create your own solutions. Text generation, a trending focus in generative AI, facilitates a broad spectrum of language tasks beyond simple question answering.

OpenAI’s ChatGPT has an extraordinary ability to process natural language, reason about a user’s prompts, and generate human-like conversation in response. However, as the saying goes, “a picture is worth a thousand words” — and perhaps an even more significant achievement is ChatGPT’s ability to understand and answer questions about images.