Deploying Your Dagster-Trained Model: Serving NanoChat on RunPod Endpoints

A walkthrough of deploying a NanoChat language model trained with Dagster to a RunPod serverless inference endpoint. Covers packaging the model into a Docker image with a serverless handler function (similar to AWS Lambda), creating RunPod templates and endpoints via a custom Dagster resource, and representing the endpoint as a Dagster asset for lineage tracking. Also shows how to build a chat inference asset that sends prompts to the live endpoint and stores results in Dagster's execution history, completing an end-to-end pipeline from raw data ingestion to interactive model serving.