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### Evolution of Artificial Intelligence into a Cloud-Based “Workload”

Considering the additional ‘strains’ that generative-AI puts on the cloud should make u…

Great solutions may fade into the background, yet they persist in the form of various software programs and data services that have started integrating highly beneficial and efficient solutions into our daily routines. Similar to essential home utilities like electricity and water supply that operate seamlessly without much thought, innovative solutions such as screen restoration features on PCs or spellcheckers in word processors seamlessly become part of our digital experience.

Despite the current hype surrounding Artificial Intelligence (AI), particularly with the emergence of generative AI (gen-AI) and the widespread adoption of Large Language Models, AI has the potential to evolve into an integrated and unobtrusive function within our everyday applications.

The Evolution of AI

In the foreseeable future, AI could transition into a standard operational “workload” within both business and consumer software, facilitating intelligent predictive, conceptual, and reactive actions. This concept has already been acknowledged within the IT industry, as highlighted in the recent enterprise AI study by Nutanix, a cross multi-cloud platform provider.

The Nutanix State of Enterprise AI Report suggests that AI’s primary role could extend to driving the adoption of hybrid multi-cloud environments, initially focusing on modernizing an organization’s IT infrastructure to better support and enhance AI capabilities. The rapid advancements in gen-AI have reshaped our perception of technology’s impact on our lives within a remarkably short timeframe. While some companies are actively exploring AI’s potential benefits, many are still in the early stages of evaluating its applications. The increasing demand for efficient data management and flexibility across various infrastructure environments underscores the importance of selecting a robust platform to seamlessly run applications and manage data across different cloud environments.

Visibility of Cloud Services

The vision of “invisible cloud” services, introduced by Nutanix before the gen-AI era, is gaining traction as businesses consider upgrading their systems and AI applications. The seamless movement of workloads, including AI, between Cloud Services Providers (CSP) hyperscalers, remains a significant challenge for many organizations. The prevalence of hybrid and multi-cloud operations reflects the evolving landscape of IT workloads, with a growing emphasis on edge computing and system optimization to meet the demands of AI technologies and the need for enhanced speed and scalability.

Greg Diamos, an expert in Machine Learning (ML) systems and AI, aptly captures the current sentiment by stating, “It’s both exciting and daunting to oversee a data center at this moment.” The evolving AI landscape necessitates greater agility in workload management across cloud platforms to capitalize on cost-effective solutions, leverage diverse services, and adapt to evolving market trends.

Unified Cloud Computing Architecture

Nutanix’s Cloud Clusters (NC2) on AWS offer a unified cloud operating model that bridges on-premises infrastructure with public cloud services, facilitating seamless application migration for companies seeking to transition to the cloud. This unified cloud operating model enables organizations of all sizes to leverage multiple cloud platforms efficiently, ensuring flexibility and cost-effectiveness in managing workloads.

The emphasis on security, reliability, and disaster recovery in AI strategies is paramount for businesses in the cloud market. The increasing need for robust AI data governance underscores the importance of understanding and monitoring data sources, data integrity, and other critical data attributes to comply with AI data regulations effectively.

Debojyoti ‘Debo’ Dutta, Vice President of Engineering for AI at Nutanix, emphasizes the necessity for new storage and data security solutions to support AI data governance. As organizations strive to enhance data protection and disaster recovery capabilities to meet AI governance requirements, the race between malicious actors leveraging AI tools for cyber threats and security professionals utilizing AI-based solutions for threat detection and prevention intensifies.

Integrating AI Workloads

While the concept of gen-AI is intriguing, its practical implementation as a cloud workload necessitates a comprehensive understanding of its impact on cloud infrastructure. As gen-AI places additional demands on cloud resources, organizations must consider the implications of running AI workloads efficiently within cloud environments to maximize performance and scalability.

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Last modified: February 22, 2024
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