Building and Scaling AI in the Cloud: Overcoming Operational Challenges with Kubernetes and FinOps

The rapid rise of generative AI and large language models is revolutionizing industries and transforming how we interact with technology. However, developing and deploying these cutting-edge AI models and applications at scale in the cloud comes with a unique set of operational challenges. In this article, we’ll explore the key pain points teams face when building generative AI and how Kubernetes and FinOps practices can help overcome them.

Optimizing GPU infrastructure utilization

Training and running large AI models requires substantial compute resources, often relying heavily on GPU resources that are expensive and in high demand. Optimizing the utilization of these resources is paramount to ensuring high performance while keeping costs under control.

Kubernetes provides a powerful platform for containerizing AI workloads and efficiently orchestrating them across available GPU instances. By packaging AI models and applications into containers, Kubernetes allocates resources at a granular level and shares GPUs among multiple workloads.

However, special attention must be given to proper GPU scheduling and preventing over or under-provisioning. Implementing node auto-scaling and intelligent pod scheduling techniques becomes crucial to rightsizing the GPU infrastructure dynamically based on the demands of AI workloads.

Enabling seamless data access

Generative AI thrives on vast amounts of data, often spanning across on-prem, multiple clouds and the edge-to-cloud continuum. Providing streamlined access to this dispersed data without excessive movement or creating silos is a significant challenge teams face.

When deploying AI workloads on Kubernetes, it’s essential to provide persistent storage volumes that can be seamlessly accessed from any node or pod. Adopting cloud-native storage solutions that offer an abstracted data access layer across clouds and on-premises data centers is key to reducing data silos and enabling efficient data utilization. By leveraging Kubernetes-native storage integrations, teams can ensure data is available to AI workloads whenever and wherever needed.

Fostering efficient team collaboration

Building successful AI initiatives requires close collaboration among data scientists, data engineers, IT operations, and DevOps teams. Enabling these diverse personas to work together efficiently is crucial for AI success.

Kubernetes offers a consistent platform and set of abstractions for deploying and managing AI workloads, promoting a shared language and understanding across teams. Data scientists can focus on developing cutting-edge models, while the operations team manages the underlying infrastructure using familiar Kubernetes constructs. By establishing consistent workflows, CI/CD pipelines, and MLOps practices on top of Kubernetes, organizations can streamline collaboration and ensure smooth handoffs between teams.

Controlling cloud costs with FinOps and automated infrastructure optimization

The dynamic and resource-intensive nature of AI and ML workloads can lead to unpredictable and rapidly escalating cloud costs. Lack of visibility and control over cloud spend is a major pain point for organizations embarking on AI initiatives.

While Kubernetes enables efficient infrastructure utilization, it can also contribute to cost management complexity due to the highly dynamic nature of containerized workloads. Implementing robust FinOps practices and tools becomes critical to monitoring and optimizing Kubernetes costs. This includes gaining granular visibility into resource consumption, rightsizing container requests and limits, and leveraging automated pod scheduling and node provisioning optimizations. Utilizing spot instances for non-production ML workloads can further significantly reduce costs.

The road ahead

As the generative AI revolution unfolds, organizations must navigate the operational challenges of building and scaling these workloads in the cloud. By leveraging the power of Kubernetes and adopting FinOps best practices, teams can overcome hurdles related to infrastructure optimization, data access, team collaboration, and cost management.

To dive deeper into the strategies and solutions for scaling AI initiatives responsibly, we invite you to download our comprehensive white paper. In partnership with IDC, this white paper explores the critical role of intelligent data infrastructure in enabling successful AI deployments.

Embrace the future of generative AI with confidence.

Read more: Scaling AI Initiatives Responsibly: The Critical Role of an Intelligent Data Infrastructure