Operations | Monitoring | ITSM | DevOps | Cloud

Operational efficiency is as critical within the IT and engineering teams as any other part of the business. Automating repetitive tasks and reducing escalations within and to these teams is of immense value. While automation saves time and boosts productivity, the complexity of developing automation can be a limiting factor and bottleneck. Generative AI is a paradigm shift here, in that it brings consumer-style simplicity to assisting in the development of enterprise-grade automation.

We’ve all been there: at that frustrating moment when you have a problem or an urgent need and the customer support line is busy. Endless searching for answers turns up nothing that matches your predicament. That’s the friction ServiceNow teams are diving into headfirst to provide each of our customers satisfactory answers with promptness that matches the urgency they feel. Generative AI is helping the teams improve customer support at a rapid pace.

In the dynamic realm of embedded systems, the right combination of hardware and software components can transform the development process and empower engineers to build robust and efficient solutions. This article explores a streamlined device-to-cloud embedded design utilizing the STM32G0 Nucleo board from STMicroelectronics, an AWS IoT ExpressLink module from Espressif, AWS IoT Core for secure MQTT communication, and Memfault for remote debugging.

Have you ever wanted to test out Grafana Cloud but don’t have any available data to monitor? Well, have no fear! With the Grafana JSON API plugin, you can query publicly available JSON endpoints. The JSON API is a wonderful way to start using Grafana Cloud. You can quickly see data in action, and there are a multitude of things you can build, analyze, and monitor using the JSON API.

Large language models (LLMs) are the topic of the year. They are as complex as they are exciting, and everyone can agree they put artificial intelligence in the spotlight. Once LLms were released to the public, the hype around them grew and so did their potential use cases – LLM-based chatbots being one of them. While large language models have been available for some time, there are still a lot of challenges when it comes to building your own project.

Artificial intelligence is certainly a hot topic right now, but what does it mean for the networking industry? In this post, Phil Gervasi looks at the role of AI and LLM in networking and separates the hype from the reality.

Artificial Intelligence (AI) is surely revolutionizing numerous industries. The AI market is projected to grow from $150 billion in 2023 to $1,345 billion in 2030, at a whopping 36.8% Compound Annual Growth Rate (CAGR). And at least 35% of companies are already using AI in their business, and an additional 42% are exploring it. However, the exhaustive list of AI business applications is still in the early stages.

Up until recently, machines mainly focused on analyzing large, existing amounts of data and finding patterns for a multitude of use cases. This is called “traditional AI.” But lately, machines have also started creating new content. And this is now known as “generative AI.” And given the rise of ChatGPT and its peers, generative artificial intelligence (AI) has quickly emerged as one of the most transformative technologies in recent years.