AI is booming. The AI market is projected to grow 37.3% annually from 2023 to 2030. With so many organizations adopting or considering AI applications, data centers need to be ready to support the new demand. However, without the right tools and data, it is difficult to understand if your existing facilities have the capacity to support systems like the “gold standard for AI infrastructure,” the NVIDIA DGX H100.

Using an RTOS is often a tradeoff between the ease of decomposing tasks, with the complexity of the scheduler itself. There exists a middle ground between highly complex systems that may require an RTOS, and simpler ones that can be easily modeled using a super loop.Since ARM is the most popular embedded CPU and almost every ARM processor has a hardware scheduler, it would be interesting to make a compact framework utilizing these features.

Over the last few years we have slowly and methodically been building out the ML based capabilities of the Netdata agent, dogfooding and iterating as we go. To date, these features have mostly been somewhat reactive and tools to aid once you are already troubleshooting. Now we feel we are ready to take a first gentle step into some more proactive use cases, starting with a simple node level anomaly rate alert. note You can read a bit more about our ML journey in our ML related blog posts.

In collaboration with F5, ServiceNow® Cloud Observability is pleased to announce the availability of the OpenTelemetry Arrow Project. This co-donated and co-developed project gives organizations greater control over the data extracted from their cloud applications—as well as a path forward to improve the return on investment (ROI) of that data.