This article explores the impact of generative AI on cloud computing infrastructure and its diverse applications across various industries. It delves into the challenges and opportunities associated with scaling generative AI workloads, data governance, and integration with existing IT systems. The article highlights the importance of containerization as a key enabler for successful generative AI implementation.
AI is both a consumer and a producer of data, a characteristic not unique to it. Business intelligence applications have long relied on ingesting data streams and application workload logs to generate insights. This pattern is mirrored across various enterprise software landscapes and IT services, where input/output defines their functionality.
However, the sheer volume of data required for a robust AI model to execute pattern recognition often surpasses even our previous definitions of 'big data.' This necessitates a profound shift in how we architect cloud computing infrastructure to meet the demands of generative AI.Nutanix, a hybrid multi-cloud computing platform company, observes the impact of generative AI at the backend (and potentially the front end) of delivery mechanisms. Projects progressing beyond prototyping and into production reveal this impact. Nutanix's annual report seeks to define the influence of current technology usage models at the input, throughput, and output stages. By analyzing these points, it offers a gauge of how 'emerging workloads' are transforming IT operations.The significance of infrastructure provision and management for AI stems from the diverse applications generative intelligence functions aim to achieve within organizations. While automation is a widely sought goal, the specific aspirations vary. Some organizations view AI as a productivity driver for existing processes, an automation engine to discover new workflows, a cost-saving tool for IT expenditures (although AI often initially increases technology stack costs), or a catalyst for experimental research leading to new product and service innovations. Nutanix posits that understanding this mix of requirements, coupled with an organization's data security, compliance, and IT infrastructure modernization strategies, can illuminate progress in this domain. Although AI projects typically entail financial investments, Nutanix SVP of product and solutions marketing Lee Caswell asserts that many organizations have reached an 'inflection point' with generative AI implementation and deployment. Despite 90% of respondents to Nutanix's analysis anticipating increased IT costs due to AI and modern application implementation, there's a silver lining. Encouragingly, almost three-quarters expect to realize a return on their AI investments within two to three years. Caswell highlights key trends gleaned from customer interactions, including challenges in scaling generative AI workloads from development to production, new requirements for data governance, privacy, and visibility, and integration with existing IT infrastructure. He emphasizes that successfully unlocking ROI with generative AI projects necessitates a holistic approach to modernizing applications and infrastructure, embracing containerization.The Nutanix market study, based on an analysis of IT executives across diverse industries, business sizes, and geographies in North and South America, Europe, the Middle East and Africa, and the Asia-Pacific-Japan region, reveals a near-universal agreement: application containerization is the new infrastructure standard. Containerization is deemed suitable for generative AI applications because it's inherently cloud-native, aligning with contemporary approaches to generative AI, which often originate and reside in the cloud. Caswell further notes that while most enterprises are now formulating generative AI strategies, implementation timelines vary significantly. This indicates that while organizations broadly anticipate generative AI solutions to enhance productivity, automation, and efficiency, real-world applications tend to be more focused. Current practical use cases for AI at this level primarily center around customer support and experience solutions
Generative AI Cloud Computing Containerization Data Governance Infrastructure Management Business Innovation Automation
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