What is a recommended practice for maintaining cluster stability in a Kubernetes-managed AI environment?

Implementing Resource Quotas and LimitRanges is essential for maintaining stability in a Kubernetes-managed AI environment. Resource Quotas allow administrators to limit the total amount of resources that can be consumed by a namespace, providing a safeguard against the excessive use of resources that could lead to system instability. LimitRanges set boundaries on individual pod resource requests and limits, ensuring that no single application can monopolize resources, which is particularly important in an environment where multiple AI workloads may be running simultaneously.

By correctly configuring these parameters, teams can manage workloads effectively, allowing for predictable performance and preventing scenarios where resource exhaustion could lead to application failures or degraded performance. This practice supports the overall health of the Kubernetes cluster, ensuring that resource allocations are balanced and that jobs can run efficiently without interference from competing workloads.

The other choices do not align with best practices for cluster stability. Increasing the number of jobs without managing resources increases the risk of resource contention, while disabling autoscaling and relying on manual job scheduling can lead to inefficiencies and increased operational overhead. These alternatives do not provide the proactive resource management that is critical for maintaining a stable, efficient operating environment in Kubernetes.