AI on private cloud: why is it relevant in the hyperscalers era?

As organisations increasingly look to take advantage of AI technologies, there are several critical considerations that they must contend with, including intellectual property, data security and costs related to computing infrastructure. Private cloud solutions are ideally suited to solving these challenges. Canonical Openstack, for instance, is a great example of a cloud platform that can be used to build and deploy machine learning applications securely and cost-effectively. Why consider a private cloud for AI? Private clouds are a handy solution for enterprises when it comes to AI/ML since they deliver many of the key capabilities that organisations report as important, including: 1) Cost optimisation: Private clouds enable businesses to optimise their costs by always running their workloads where it makes more sense from an economic standpoint. 2) Digital sovereignty: Private clouds offer a safe environment for data and applications by ensuring that the organisation owns access and controls the level of sharing amongst the different teams using the cloud. 3) Performance acceleration: Private clouds offer GPU virtualisation and other capabilities to improve performance and therefore project delivery, confidentiality, efficiency and time to setup as required by sophisticated AI/ML workloads. Learn more about AI on the private cloud in this webinar: Join the webinar on 21 February 2023, where Tytus Kurek, OpenStack Product Manager, and Andreea Munteanu, AI Product Manager, will talk more about private clouds for AI projects. The presentation will cover: 1) Key considerations when building a private cloud for AI projects 2) Performance acceleration options for private cloud 3) Guidance for Kubernetes on OpenStack for AI initiatives And more…