Operations | Monitoring | ITSM | DevOps | Cloud

To ease the path of enterprise AI adoption and accelerate the conversion of AI insights into business value, NVIDIA recently published the NVIDIA Enterprise AI Factory validated design, an ecosystem of solutions that integrates seamlessly with enterprise systems, data sources, and security infrastructure. The NVIDIA templates for hardware and software design are tailored for modern AI projects, including Physical AI & HPC with a focus on agentic AI workloads.

Across the Asia Pacific region significant investment is going into new subsea cable infrastructure that will help sustain the long-term demand for AI. We’ve written a lot on this blog about the impact of AI on networks and how AI workloads require low latency, high-capacity data transfer. This in turn puts more pressure on existing network infrastructure and in particular subsea cable systems - which provide the global backbone for cloud platforms and data centres.

Sovereign AI ensures control over AI/ML data, models, and infrastructure, which is now essential for enterprises, regulated industries, and national interests. JFrog and NVIDIA have collaborated to deliver a secure, scalable solution for sovereign AI. NVIDIA provides the accelerated computing and AI software while JFrog ensures trusted DevSecOps and MLOps practices across the entire AI lifecycle, from model development and security scanning to deployment at the edge and in air-gapped environments.

The latest Alloy mobile app updates are here, featuring push notifications in the Alloy Self-Service app and more to keep you productive on the go this summer!

Getting paged pulls engineers away from meaningful work, yet incident response in many organizations remains manual, reactive, and draining. An alert fires and teams scramble to find the root cause, relying on siloed knowledge, incomplete context, and a few on-call experts who are already stretched thin. The rise of AI coding agents has only intensified this challenge: As teams ship code faster with less human oversight, production systems grow increasingly complex and harder to understand.

To efficiently optimize your LLM application before pushing to production, you need a comprehensive testing and evaluation framework. By running experiments, you can optimize prompts, fine-tune temperature and other key parameters, test complex agent architectures, and understand how your application may respond to atypical, complex, or adversarial inputs. However, it can be difficult to manage your experiment runs and aggregate the results for meaningful analysis.