If you’re an infrastructure or MLOps engineer at a large company, you know the drill. The ML team comes to you with requirements that change weekly. They need GPUs yesterday, but the budget was set six months ago. They want to use the latest framework, but it breaks your carefully crafted Kubernetes deployments. They need to comply with data locality requirements while also optimizing for cost. Sound familiar? You’re not alone, and there’s a better way.
