Automatic Infrastructure for A.I.

Last week's article discussed the critical decision-making process between building custom AI applications or opting for off-the-shelf solutions to tackle business challenges. Continuing on this journey, this article zooms in on the pivotal characteristics of the infrastructure required for running AI workloads effectively. Regardless of whether your preference leans towards the public cloud, on-premises infrastructure, or a hybrid approach, certain foundational capabilities are essential for not just short-term triumphs but also long-term adaptability and sustainability in AI workload operations. The more fluid the infrastructure for AI the greater the ability to prototype, test, deploy, and operationalize these value-driving workloads.

Be water, my friend. - Bruce Lee

Compute Infrastructure

GPUs have added a lot of allure to the overall world of compute, but the servers that house the CPUs and GPUs needed to run the AI workloads still need lifecycle management. However, behind the scenes, servers housing CPUs and GPUs still require lifecycle management. While leveraging the public cloud for certain workloads can simplify the compute infrastructure layer, organizations mandated to run AI workloads on-premises due to security, compliance, or legal considerations must grapple with the operational implications of nurturing a new server farm tailored for these demands. Patch management, BIOS, and firmware updates are all part of managing compute infrastructure. Operational efficiency becomes paramount for an environment poised for considerable scale-out expansion over time.

Is the platform adaptable enough to scale out CPUs and GPUs without requiring significant re-tooling to your infrastructure operations runbook?

Storage

At the heart of every AI solution lies data. Data fuels model training, fine-tuning, and serves as the output of generative AI solutions. Pinpointing a scale-out storage platform capable of meeting the performance challenge of AI workloads, while also offering ease of management emerges as a critical priority. AI solutions are often iterated numerous times in the prototyping phase so the simplicity of provisioning and archiving also becomes indispensable.

Does the storage platform require deep storage expertise or can it be provisioned and managed by the core infrastructure team?

Containers

For infrastructure teams accustomed to wrangling virtual machines, venturing into the realm of containers represents a burgeoning area of professional growth. Given that AI applications are typically through containers, many VM administrators confront the challenge of managing an expanding footprint of containers. Identifying a platform empowering existing personnel to navigate this burgeoning landscape of containers holds the key to ensuring operational proficiency across the AI stack.

Can your existing IT team support the growing demand for containers in the AI world?

Machine Learning Framework

Machine learning frameworks serve as the scaffolding enabling data scientists and AI developers to expedite the construction and deployment of machine learning models. When charting the AI infrastructure roadmap for your organization, it's important that ML frameworks can be seamlessly deployed by individuals who might not have deep ML expertise. In many organizations, the Infrastructure Team shoulders the responsibility of delivering and supporting the foundational infrastructure necessary for AI workloads, to include the deployment of ML frameworks.

Does the infrastructure support 1-click deployment of ML frameworks or does it require 3rd party professional services?

Adaptability

AI is a dynamic marketplace teeming with new technologies and capabilities unveiled with each passing month. Does your AI infrastructure exhibit adaptability? Can you seamlessly embrace GPUs (and CPUs)? Does managing the lifecycle of a test/dev environment, characterized by high rates of provisioning and decommissioning, pose a logistical challenge? Do you have an AI infrastructure partner boasting a clear vision poised to support AI workloads not just today but well into the future?

People

In my interactions with various organizations, there is pervasive enthusiasm from lines of business leaders towards embracing AI. Similarly, the excitement within Infrastructure teams as they gear up to support these initiatives signals the dawn of a remarkable new era in infrastructure operations: infrastructure tailored for AI. Yet, amidst the fervor, a tinge of apprehension festers within infrastructure teams, stemming from the perceived influx of new responsibilities. Platforms adept at translating existing skills into competencies relevant for managing AI infrastructure are poised to expedite the journey to value realization for most organizations. For instance, a platform equipped with container management capabilities as intuitive as managing virtual machines can bridge this gap seamlessly.

In Closing

As we navigate the ever-evolving landscape of AI infrastructure, I like to remain guided by the ethos encapsulated in Bruce Lee's iconic words, "Be water, my friend."

Adaptable. Ready to support and partner with lines of business leaders as they drive transformational change and value with AI.

By embracing compute infrastructure bolstered by GPUs, flexible storage solutions, simplified container management, versatile machine learning frameworks, unwavering adaptability, and a people-centric approach, CIOs can guide their organizations to value realization through AI-powered innovation and sustained success.

🔑 takeaway: when choosing an infrastructure for AI workloads consider not just immediate capabilities but overall sustainability of operating and managing the infrastructure.