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AI’s Transition into a Cloud Workload

TL;DR

  • AI’s transition into a “workload” is reshaping IT infrastructure and cloud strategies.
  • The rise of generative AI is driving increased demand for cloud services and agility.
  • A unified cloud operating model and robust data governance are crucial for managing AI workloads in a multi-cloud environment.

In the ever-evolving landscape of technology, truly useful and effective innovations tend to fade into the background as they seamlessly integrate into our daily routines. Consider the spellchecker in your word processor or the screen refresh utility on your PC – these technologies have become so ingrained that we rarely think about them. 

Artificial Intelligence (AI), however, is currently experiencing the opposite trajectory. Rather than fading into the background, AI is basking in the limelight due to the advent of generative AI (gen-AI) and the widespread proliferation of Large Language Models. Nevertheless, AI has the potential to transform into an assumed, consumed, and subsumed function that silently enhances the intelligence of our applications.

AI as a system workload

The concept of AI as a “workload” is gaining traction within the IT industry. This term refers to AI functioning as a system task that enterprises and consumers rely on for smart predictive, generative, or reactive actions. AI’s role is not limited to improving applications but also extends to modernizing an organization’s IT infrastructure to better support and scale AI workloads.

Sammy Zoghlami, SVP EMEA at Nutanix, explains, “In just one year, gen-AI has completely upended the worldview of how technology will influence our lives. Enterprises are racing to understand how it can benefit their businesses.” This shift has prompted a growing demand for data governance and data mobility across data center, cloud, and edge infrastructure environments, making it crucial for organizations to adopt a platform capable of running all applications and data across various clouds.

Invisible cloud services and the movement of workloads

The notion of “invisible cloud” services, which Nutanix introduced last year, is gradually becoming a reality. Enterprises are now planning to upgrade their AI applications and infrastructure, but many struggle with workload movement, especially between Cloud Service Providers (CSP) hyperscalers. Hybrid and multi-cloud deployments have become the norm, and AI technologies, with their need for speed and scalability, are pushing edge strategies and infrastructure deployment to the forefront of IT modernization.

Greg Diamos, a Machine Learning (ML) systems builder and AI expert, notes the challenges datacenter managers face, stating, “You don’t have enough computing in your data center, no matter who you are.” AI is driving a need for increased cloud services and greater agility in moving workloads across the cloud landscape to optimize cost-performance ratios, leverage diverse services, adhere to regional compliance regulations, and more.

A unified cloud operating model

To address these challenges, organizations are turning to solutions like Nutanix Cloud Clusters (NC2) on AWS, which provide a unified cloud operating model. This model enables seamless management and control of workloads across multiple clouds, supporting the portability of licenses and facilitating cloud usage without the need for extensive application re-architecting.

Security, reliability, and disaster recovery are paramount concerns for organizations in their AI strategies. Scaling and managing AI workloads efficiently are also essential. Additionally, AI data governance mandates are prompting organizations to gain a more comprehensive understanding of data sources, data age, and other key data attributes.

Debojyoti ‘Debo’ Dutta, VP of Engineering for AI at Nutanix, underscores the need for new backup and data protection solutions in the AI landscape. Companies are planning to implement mission-critical data protection and Disaster Recovery (DR) solutions to support AI data governance. Security professionals are also leveraging AI-based solutions to enhance threat detection, prevention, and recovery while bad actors use AI-based tools for malicious purposes.

The role of generative AI

Generative AI (gen-AI) is at the forefront of AI’s current advancements. However, as gen-AI becomes an integral part of various applications and services, it places additional demands on the cloud infrastructure. This development prompts us to consider AI not merely as a technology but as a cloud workload that requires thoughtful management and optimization.

As AI continues to shape the future of technology, its seamless integration into our digital ecosystem will make it an essential, yet nearly invisible, part of our daily lives.

The trajectory of AI’s evolution from a disruptive innovation to a ubiquitous, cloud-based workload is a testament to its transformative potential. As organizations navigate the complexities of managing AI workloads across multi-cloud environments, a unified cloud operating model and robust data governance become critical components of their AI strategies. In this ever-changing landscape, AI’s role as a silent, powerful force shaping our digital world is only set to grow.

Disclaimer. The information provided is not trading advice. Cryptopolitan.com holds no liability for any investments made based on the information provided on this page. We strongly recommend independent research and/or consultation with a qualified professional before making any investment decisions.

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Editah Patrick

Editah is a versatile fintech analyst with a deep understanding of blockchain domains. As much as technology fascinates her, she finds the intersection of both technology and finance mind-blowing. Her particular interest in digital wallets and blockchain aids her audience.

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