The rapid rise of AI services has created a massive demand for computing resources, making efficient management of those resources a critical challenge. While running AI workloads with Kubernetes has come a long way, optimizing scheduling based on dynamic demand continues to be an area for improvement. Many organizations face constraints related to the cost and availability of GPU clusters worldwide and often rely on the same compute clusters for inference workloads and continuous model training and fine-tuning.  

 AI Model Training and Model Inferencing in Kubernetes

Training typically requires far more computational power than inferencing. On the other hand, inferencing is far more frequent than training as it is used to make predictions repeatedly across many applications. Let’s explore how we can harness the best of what the cloud has to offer with advances in Kubernetes to optimize resource allocation by prioritizing workloads dynamically and efficiently based on need.