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A vector database is a data storage system that organises information in the form of vectors, which are mathematical representations. These databases are designed to store, index, and query vector embeddings or numerical representations of unstructured data, including text documents, multimedia content, audio, geospatial coordinates, tables, and graphs.

In the world of cloud computing, we rely on abstraction layers to manage complex systems. While this simplifies development, it also creates vulnerabilities for sensitive data. Traditionally, privileged software within the cloud has access to your data, and could pose a significant security risk, if not managed properly. But there’s a new way to protect your data: confidential computing.

Microsoft Azure has announced the general availability of their confidential virtual machines (VMs) with NVIDIA H100 Tensor Core GPUs, powered by Ubuntu. This offering combines the hardware-based protection of AMD EPYC processors with NVIDIA’s latest GPU technology to enable secure and high-performance AI workloads in the cloud. Combining these technologies allows sensitive sectors to unlock AI adoption through addressing previous concerns of critical data privacy.

The EU Cyber Resilience Act is coming. I’ve talked about this piece of upcoming regulation in some depth before, having covered its background and stipulations in previous pieces on our website and for the Forbes Technology Council, and explored what it means for the businesses who consume open source in later articles (you can also read a version of this blog on Forbes).

Cédric Gégout (VP of Product at Canonical) reflects on a recent research report that Canonical published in collaboration with Omdia. The report uncovered that despite the lack of widespread 5G standalone infrastructure, enterprises, industrial actors and service providers are preparing for 5G edge computing to produce returns within two years.

AI is driving a new wave of opportunities in all kinds of edge settings—from predictive maintenance in manufacturing, to virtual assistants in healthcare, to telco router optimisation in the most remote locations. But to support these AI workloads running virtually everywhere, companies need edge infrastructure that’s fast, secure and highly scalable.

The accelerated developments in machine learning and artificial intelligence in healthcare have set the stage for some interesting transformations. By enabling better care for patients, optimizing processes, and generating new opportunities for medical research and treatment, these technologies are indeed going to change the healthcare industry radically.

Recently, a striking report published by Omdia and Canonical highlighted that 86% of communication service providers (CSPs) are optimistic about the future of edge computing on telco networks. This is a market that is expected to grow substantially in the coming years, with our report shedding light on the motivation that CPSs are drawing from the enhancements that 5G networks will bring.

This blog post is part of our data centre networking series.

Canonical is excited to announce that we are collaborating with OpenAirInterface (OAI) to drive the development and promotion of open source software for open radio access networks (Open RAN). Canonical will bring automation in software lifecycle management to OAI’s RAN stack, alongside additional infrastructure capabilities. This will better enable telcos to adopt open source software as the telecom industry transitions to Open RAN running on COTS hardware.