Good technologies are disappearing. Although they are mostly still here, truly useful and effective technologies are starting to slip into the fabric of other software tools and data services that we all use every day. Almost like a public service that we don’t really think about (who wonders about the state of the electricity network when they turn on the lights, or thinks about the water company’s supply pipes when they run a bath? ) good technologies like the spell checker in your word processor or the screen refresh utility on your PC are absorbed almost invisibly.
This process has not yet happened with artificial intelligence (AI) – it attracts far too much noise and enjoys its time in the spotlight thanks to the arrival of generative AI (gen-AI) and the proliferation of large language models – but AI has the potential destiny of becoming an assumed, consumed and encompassed function that makes all our applications smarter in a pleasantly automated way.
AI as a workload
If that time comes, we’ll start talking about AI itself as a system “workload,” that is, a function that our enterprise or consumer software runs to perform actions predictive, generative or reactive intelligent on our behalf. In fact, the IT industry has already started using this term. It surfaced in the last enterprise AI study by hybrid multi-cloud platform company Nutanix.
The Nutanix State of Enterprise AI report suggests that AI will now be a workload that advances hybrid multi-cloud adoption. Its first job – even before working on the applications you have in your pocket – will be focused on modernizing an organization’s IT infrastructure, which will often need to be improved to more easily support and scale IT workloads. AI work.
“In just one year, the AI generation has completely changed the world’s view of how technology impacts our lives. Companies are scrambling to understand how this can benefit their businesses,” said Sammy Zoghlami, SVP EMEA at Nutanix. “While most organizations are still in the early stages of evaluating this opportunity, many view it as a priority. (Our) survey revealed an important theme among companies adopting AI solutions: a growing requirement for data governance and mobility across data center, cloud and edge infrastructure environments, which which makes it even more important for organizations to adopt one platform to run all applications and data across all environments. clouds.”
Invisible cloud services
It was last year (even before the AI generation) that Nutanix talked about a dream vision for what we call “invisible cloud‘ services, so this theme is undoubtedly beginning to be validated and take shape. This year, the company says it is targeting businesses that are now considering upgrading their AI applications or infrastructure. Where some companies struggle to achieve this is in many areas, but the movement of workloads (AI and otherwise) between cloud service provider (CSP) hyperscalers is usually among the usual suspects.
Today, hybrid and multi-cloud deployments are well established and synonymous with modern IT infrastructure workloads. AI technologies, along with increasing demands for speed and scalability, will likely put edge strategies and infrastructure deployment at the forefront of IT modernization.
“It’s probably both exciting and terrifying to be a data center manager right now,” Greg Diamos, machine learning (ML) systems builder and AI expert. “You don’t have enough computing resources in your data center, no matter who you are. » Diamos’ comment was made in the context of the Nutanix report and the broader proposition that AI itself is driving a need for a) spiraling cloud services and b) greater agility to move workloads across the cloud landscape (for lack of a cloudier skyward analogy) to capture value-for-money deals, utilize diversified services, comply with local regional compliance legislation, and more.
A unified cloud operating model
Organizations now looking to migrate their existing applications to the public cloud can use Nutanix Cloud Clusters (NC2) on AWS, which provides the same on-premises cloud operating model as in the public cloud. This is all part of what the company likes to call its notion of a unified cloud operating model, which is to say that most organizations of any reasonable size will inevitably use more than one cloud, so they need a management model to enable this control factor.
“Customers can accelerate cloud usage without going through the time-consuming and costly process of rearchitecting an application,” Zoghlami said. “Nutanix licenses are truly portable, meaning customers can choose where to run their applications and move them later if necessary, without having to purchase new licenses. Customers can also use their existing AWS credits and purchase licenses from the AWS Marketplace.
In the company’s cloud market research, almost all organizations say security, reliability and disaster recovery are important considerations in their AI strategy. The need to manage and support AI workloads at scale is also critical. In the area of AI data decisions and regulation, many companies believe that AI data governance requirements will require them to more thoroughly understand and track data sources, age data and other key data attributes.
“AI technologies will drive the need for new data backup and protection solutions,” said Debojyoti “Debo” Dutta, vice president of engineering for AI at Nutanix. “(Many companies) are considering adding critical data protection and disaster recovery (DR) solutions at the production level to support AI data governance. Security professionals are rushing to use AI-powered solutions to improve threat and anomaly detection, prevention and recovery, while malicious actors are rushing to use AI-driven tools to create new malicious applications, improve success rates and attack surfaces, and improve detection avoidance.
Generative AI in motion
While it’s okay to “invent” AI generation, implementing it obviously means thinking about its existence as a cloud workload in itself. While cloud computing is still poorly understood in some circles and the cloud native epiphany is not shared by all businesses, given the additional constraints (for lack of a gentler term) that the AI generation places on the cloud, we should think of AI as a cloud workload. more directly and think about how we manage it.