A study of 4 major use cases of AI in cars In this fast-paced age of technological evolution, Artificial Intelligence (AI) emerges as the key catalyst driving profound shifts in the automotive sector. From smart vehicle design to customised in-car interactions, AI is reshaping every aspect of transportation, ensuring safer, more effective, and environmentally friendly journeys for both drivers and passengers.

I recently wrote about how AI can support unified communications in the enterprise, but its applications are much broader than that, and communications service providers also have the opportunity to benefit. In a recent Gartner survey, 95% of telecom CIOs said they plan to implement AI/ML technologies by 2026 and 79% said they plan to increase their AI/ML investments.

Generative artificial intelligence (AI) represents a new frontier for transformative productivity. With over 300,000 customers worldwide harnessing our data-driven solutions, SolarWinds is well-positioned to leverage AI to enrich the lives of the IT professionals we serve. But for this exciting technology to yield real value over time, it’s crucial to build it sustainably.

As AI and machine learning workloads continue to grow in complexity and size, the need for efficient and scalable infrastructure becomes more important than ever. In this tutorial, I will show you how to deploy AI applications on AWS Elastic Kubernetes Service (EKS) with Karpenter from scratch, leveraging GPU resources for high-performance computing.

I recently had the privilege of attending KubeCon + CloudNativeCon Europe 2024 in Paris. The conference, hosted by the Linux Foundation, marked the 10th anniversary of Kubernetes. Here are the key takeaways and highlights from the conference.

Top tips is a weekly column where we highlight what’s trending in the tech world today and list out ways to explore these trends. In this week’s edition, we’re going over how generative AI can help content creators improve the quality of the content they put out. ChatGPT’s release in November 2022 launched generative AI to the very forefront of the technological landscape. For a few months after that, it seemed like all people were talking about was generative AI (GenAI).

Just a few years back, developing AI/ML (Machine Learning) models was a secluded endeavor, primarily undertaken by small teams of developers and data scientists away from public scrutiny. However, with the surge in GenAI/LLMs, open-source models, and ML development tools, there’s been a significant democratization of model creation, with more developers and organizations engaging in ML model development than ever before.

Earlier this month, I had the pleasure of traveling to Nuremberg, Germany to attend Embedded World. If you have not heard about it before, Embedded World1 is the largest trade show in the embedded systems industry. This year, over 35,000 people attended and 1,100 businesses exhibited at the Nuremberg Messe.