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The need for Platform Engineering in ML/AI Operations

As the field of Artificial Intelligence (AI) and Machine Learning (ML) Operations matures, it is necessary to transition from the pioneering phase to a more structured and systematic approach, similar to the evolution seen in general software development with the advent of Platform Engineering movement, to enhance operational efficiency, reduce costs, and streamline processes in ML/AI operations.

Wednesday, January 31, 2024 Adriano Pezzuto

The paradox of power in AI

Imagine owning a powerful, shining, and expensive Ferrari and getting stuck in a traffic jam: this is the current landscape in Artificial Intelligence (AI) and Machine Learning (ML) operations. Hardware accelerators, also known as Graphical Processing Units (GPUs), have become the horsepower of computing in AI and ML. However, despite their critical role, the industry faces two pressing challenges, apparently contradicting each other: scarcity and underutilization of GPUs. This inefficiency not only causes costs to skyrocket but also causes obstacles to the broader adoption and evolution of AI technologies.

GPUs Scarcity: An Expensive Business

Demand for GPUs in the AI/ML industry has soared, driven by growing needs, supply chain disruptions due to geopolitical issues, and competition from other sectors such as gaming and cryptocurrencies. This surge has inevitably led to increased GPUs costs, making an outright purchase challenging for most organizations. In response, many are turning to leasing GPUs from hyperscalers or specialized cloud providers, a practical but complex solution that requires a different approach than typical cloud services.

GPUs Underutilization: The Hidden Challenge

As ML/AI projects proliferated, the approach to managing computational resources, especially GPUs, didn’t evolve at the same pace. This resulted in significant infrastructural inefficiencies. Many AI teams found themselves in a situation where, despite having access to high-powered GPUs, they were only utilizing a fraction of their capabilities. This underutilization comes from several factors:

  • Mismatched Resource Allocation

    Often, GPUs are allocated based on estimations rather than real-time needs, leading to situations where some teams have excess resources while others have too few.

  • Lack of Effective Resource Sharing

    Without a system to dynamically allocate and deallocate shared resources based on current workload demands, GPUs remain tied up in idle or low-priority tasks.

Competing for GPUs Resources Within Organizations

Another facet of this challenge is the internal competition for GPU resources. In many organizations, different teams working on various AI/ML projects find themselves in a race to secure GPUs access. This competition can lead to:

  • Workflow Delays

    As teams wait for their turn to access GPUs, project timelines get extended, impacting the overall efficiency and productivity of the AI initiatives.

  • Misaligned Priorities

    Without a centralized management system, resource allocation often becomes a matter of who has the most immediate need or who can make the most compelling case, rather than what aligns best with organizational priorities.

To address these challenges, a paradigm shift in GPUs resource management is essential. The need is for a more strategic, flexible, and efficient approach to GPUs utilization.

Platform Engineering for ML/AI

Platform Engineering (PE) is the practice of creating a standardized, scalable, and efficient infrastructure and set of tools to support the development, deployment, and management of applications across an organization.

Platform Engineering has emerged recently into software development and DevOps environments as a common way of running operations. This concept, when translated into the realm of ML/AI, addresses a crucial need: establishing a systematic, organized approach to managing ML/AI operations.

A Platform Engineering approach to ML/IA Operations would overcome the paradox of GPUs underutilization. It introduces more dynamic and automated resource management strategies, ensuring that GPUs are dynamically allocated, based on the needs, and optimally used in a secure multi-tenant environment.

The role of Kubernetes

Developers have embraced the Kubernetes ecosystem for their ability to abstract modern distributed applications from the infrastructure layer. This ecosystem, renowned for efficiently managing and abstracting complex, distributed applications from the underlying infrastructure, becomes the key to the Platform Engineering approach for ML/AI.

Automated and Dynamic Resource Allocation: Kubernetes excels in replacing static resource allocation with a dynamic and automated system. Its intelligent scheduling capabilities ensure that GPUs are allocated precisely where and when needed, maximizing their usage and efficiency. This approach directly addresses the problem of underutilized GPUs, ensuring that these powerful resources are fully leveraged for AI/ML workloads.

Efficient Multi-Tenancy Management: At Clastix, we focus on creating efficient and secure multi-tenant Kubernetes platforms. Our multi-tenancy solutions as Capsule and Kamaji are suitable to meet the unique demands of AI infrastructures. By building a multi-tenancy platform at the Kubernetes level, we enable multiple teams or research units within an organization to share resources effectively while maintaining essential isolation and security.

Clastix Solutions for ML/AI Operations

Isolation and Advanced Resource Quota Management: Our solutions offer varying isolation levels, from dedicated resources for specific projects to shared clusters with logical resource quotas, ensuring optimal resource distribution without compromising security or performance.

Centralized Management and Enterprise Security: The platform operators gain centralized control, ensuring efficient management and compliance across all operations while maintaining data privacy and security for all tenants.

Efficient Resource Utilization: Advanced scheduling capabilities and GPUs sharing features, such as fractional GPUs and dynamic Multi-Instance GPU (MIG) configurations, enable more efficient sharing and utilization of resources among different tenants and workloads.

An Outlook for the Future

The integration of Kubernetes within the ML/AI Operations, guided by Platform Engineering principles, is a significant evolution in the industry. By streamlining the allocation and utilization of GPUs, Kubernetes is paving the way toward more innovative, efficient, and impactful AI solutions. This unified approach resolves the issue of GPUs underutilization and aligns AI operations more closely with organizational goals and strategies.

Start transforming your ML/AI Operations with Kubernetes and Platform Engineering principles today. Embrace efficiency, collaboration, and strategic alignment in your Artificial Intelligence (AI) and Machine Learning (ML) initiatives. Reach out to our team to explore how we can guide you on this transformative journey and unlock the full potential of your projects.