How to deploy and fine-tune DeepSeek models on AWS

Deploying DeepSeek on AWS isn't rocket science, but setting up the MLOps pipeline is the actual headache. We've got the infrastructure stuff sorted—SageMaker, EC2, Kubernetes—but fine-tuning reliably requires serious GPU oversight and managing those massive parameter counts.

I don't see a simple tutorial that covers the real deployment pain points, like handling distributed training across multiple GPUs efficiently or managing the data drift post-deployment. It's a massive operational burden, not just a code copy-paste job. You'll spend more time wrangling infrastructure than tuning the model itself.