Overcoming AI/ML Management Challenges on Kubernetes

Written by Itiel Shwartz, CTO and co-founder of Komodor

While Kubernetes was not designed originally for AI/ML workloads, it has gained popularity for this due to its flexibility and ecosystem. However, managing AI/ML workloads on Kubernetes comes with significant challenges. 

That’s because deploying AI/ML workloads on Kubernetes typically involves a three-stage process -- data preparation, data training, and model serving -- which is resource-intensive and requires careful orchestration.

Data preparation, including collection, cleaning, and transforming, is resource-intensive, especially with large datasets requiring distributed processing. Tools like Apache Airflow and Argo Workflows on Kubernetes can assist but also add complexity. Training the AI model demands significant computational power, often using multiple GPUs, and while Kubernetes handles distributed systems well, it introduces issues with resource allocation. Model serving, where the trained model makes predictions, varies from batch processing to real-time inference. Kubernetes scales well for serving but maintaining resource efficiency and consistent performance becomes more difficult in production.

Understanding the Management Challenges

Unlike stateless applications that can scale horizontally easily, AI/ML workloads are often stateful, memory-intensive, and require dedicated hardware such as GPUs. The workloads’ dynamic and spiky nature can lead to significant variations in resource demands, making it challenging to allocate compute and memory resources efficiently. This often results in some nodes being underutilized while others are overwhelmed, causing performance bottlenecks, inefficiencies, and cost overruns. 

Related:A Guide to Storage for AI Workloads

Kubernetes also lacks inherent guardrails for managing AI/ML workloads. The platform’s flexibility, while beneficial, comes at a cost. Teams often struggle to maintain consistent performance without built-in mechanisms to enforce best practices—such as ensuring efficient GPU usage or optimizing memory allocation. This challenge can lead to suboptimal resource usage, higher operational costs, and difficulties in scaling AI/ML workloads effectively. 

Security is another critical concern. As AI/ML workloads often involve handling sensitive data in production environments, it is essential to govern access carefully. Data engineers should have access to the necessary resources without being burdened with cognitive load. This can be accomplished by providing a customized workspace that only exposes resources and functions they need to perform their tasks and enforces robust security controls. 

Related:How to Fix OOMKilled Errors in Kubernetes

Troubleshooting AI/ML workloads on Kubernetes is especially difficult. The complexity of AI/ML workloads, coupled with Kubernetes’ distributed architecture, makes diagnosing and resolving issues particularly challenging. Common problems include failed workflows caused by resource contention, difficulties in identifying performance bottlenecks, and challenges in maintaining high availability for critical workloads. 

For example, it can be extremely time-consuming to investigate a workflow failure caused by a node being autoscaled down when workflow pods run on it. So, while Kubernetes offers robust logging and monitoring tools for stateless applications, these tools are often inadequate for the needs of AI/ML workloads, requiring additional customization and expertise. 

To complicate matters, there is a noticeable gap in the availability of experienced staff who can effectively manage Kubernetes running AI/ML workloads. The intersection of AI/ML and Kubernetes requires a deep understanding of data engineering, data science, and cloud-native architectures. However, many organizations find that their teams are proficient in one domain but not both, leading to longer learning curves, increased downtime, and a higher likelihood of mistakes that can negatively impact performance and reliability.

Related:GPU Costs Aren't Infinitely Scalable — What Can Be Done?

Best Practice Guidelines 

To overcome these challenges, organizations should consider the following best practices:

Deploying AI/ML workloads on Kubernetes unlocks tremendous potential for scalability and efficiency, yet it also introduces significant management complexity. By thoroughly understanding these challenges and proactively implementing targeted best practices, organizations can achieve smoother operations and enhanced performance and take full advantage of Kubernetes for running AI/ML in the cloud.

About the Author 

Itiel Shwartz, the CTO and co-founder of Komodor, is an expert in Kubernetes, cloud-native technologies and infrastructure. He has served in technical leadership roles at eBay, Forter, and Rookout.