How AI evolved into a cloud ‘task’

Valuable technologies fade into obscurity. While they still exist in essence, truly beneficial and efficient technologies are now being assimilated into the fabric of other software tools and data services that we use on a daily basis. They are almost akin to a household utility that operates almost unnoticed (who ponders over the state of the power grid when they flick the light switch, or the water company’s supply lines when they take a shower)? Good technologies, like the spell checker in your word processor or the screen refresh utility on your PC, are seamlessly integrated.

This phenomenon has not yet manifested with Artificial Intelligence (AI) – it is currently receiving considerable acclaim and basking in the limelight owing to the emergence of Generic AI (Gen-AI) and the widespread adoption of large language models. However, AI has the potential to become an envisioned, consumed, and integrated function that enhances the intelligence of all our applications in a wonderfully automated manner.

AI as a task

If that time comes, we will begin to refer to AI itself as a system ‘task’ – i.e. the function that our enterprise or consumer software performs to execute intelligent predictive, generative, or reactive actions on our behalf. The IT industry has already started using this term. This is illustrated in the latest enterprise AI study from hybrid multi-cloud platform company Nutanix.

The Nutanix State of Enterprise AI report reveals that AI will now constitute a task that drives the adoption of hybrid multi-cloud. This primary task – preceding development on applications in your pocket – will concentrate on modernizing an organization’s IT infrastructure to more readily support and scale AI tasks. Amendments will frequently be necessary.

“In just one year, Gen-AI has completely overturned the worldview of how technology will impact our lives. Enterprises are scrambling to comprehend how this can benefit their businesses,” remarked Sammy Zoghlami, SVP of EMEA at Nutanix. “While most organizations are still in the initial stages of evaluating the opportunity, many consider it a priority. [Our] The survey underscored a key theme among enterprises adopting AI solutions: the escalating need for data governance and data mobility across datacenter, cloud, and edge infrastructure environments has driven organizations to adopt a single platform to run all apps and data in the cloud. Made even more important.

Concealed Cloud Services

Last year (even before Gen-AI), Nutanix articulated a utopian vision for so-called ‘concealed cloud’ services; therefore, this subject is arguably starting to validate itself and take shape. This year, the company asserts it is engaging with enterprises that are now contemplating upgrading their AI applications or infrastructure. While some companies confront challenges in numerous areas, the migration of tasks (AI and others) between cloud services provider (CSP) hyperscalers typically emerges as one of the common culprits.

Presently, hybrid and multi-cloud deployments are firmly established and synonymous with modern IT infrastructure tasks. AI technologies, along with increasing demands for speed and scale, are likely to propel leading strategies and infrastructure deployments to the forefront of IT modernization.

“Being a datacenter manager right now is probably simultaneously exhilarating and daunting,” remarked Greg DiMos, a machine learning (ML) system architect and AI specialist. “Regardless of who you are, your datacenter likely lacks sufficient computing power.” DiMos’ comments were made in the context of the Nutanix report and the broader argument that AI itself is driving the need for a) expanding cloud services, and b) heightened agility in migrating tasks across cloud landscapes (toward a metaphorical cloudy sky analogy for the pursuit of value-performance deals, utilization of diverse services, compliance with regional legislation, etc).

A unified cloud operating model

Organizations seeking to migrate their existing applications to the public cloud can now utilize the Nutanix Cloud Cluster (NC2) on AWS, which furnishes the same cloud operating model as on-premises, akin to the public cloud. This is all part of what the company dubs its concept of a unified cloud operating model – i.e. most organizations of any reasonable size will inevitably employ more than one cloud, necessitating a management factor to enable that requisite control. Model required.

“Customers can commence using the cloud without undergoing the expensive and time-consuming process of building new applications,” Zoghlami affirmed. “Nutanix licenses are genuinely transportable, allowing customers to choose where to deploy their applications and relocate them later as necessary, without the need to acquire new licenses. Customers can also employ their existing AWS credits and procure licenses on the AWS Marketplace.

In the company’s cloud market study, nearly all organizations assert that security, reliability, and disaster recovery are pivotal considerations in their AI strategy. The imperative to manage and support large-scale AI tasks is also critical. In the realm of AI data governance, numerous companies opine that AI data governance demands will compel them to gain a more comprehensive understanding of and track data sources, data age, and other key data characteristics.

“AI technologies will induce the need for new backup and data protection solutions,” stated Debojyoti ‘Debo’ Dutta, vice president of AI engineering at Nutanix. “[Many companies are] planning to incorporate mission-critical, production-level data security and disaster recovery (DR) solutions to support data governance. Security professionals are racing to utilize AI-based solutions to enhance threat and anomaly detection, prevention, and recovery, while malicious actors are crafting new malicious applications, enhancing success rates and attack surfaces, and evading detection.”

Generative AI is in progress

While it is fine to ‘conceive’ Gen-AI, the act of putting it into motion inevitably entails contemplating its existence as a cloud task in its own right. Cloud computing is still misconstrued in certain circles and the cloud-native realization is not universally embraced by every company, considering the additional strain (for lack of a milder term) that Gen-AI will exert on the cloud. This should prompt us to regard AI as a cloud task, and contemplate how to manage it more effectively.