How Doppel eliminated ML infrastructure tax with Modal | Modal Blog

This post details how Doppel migrated their ML training and inference workflows to Modal. For training, they leveraged Modal's `map()` function to parallelize K-fold cross-validation, significantly reducing experimentation time compared to sequential runs. They also integrated coding agents with Modal's CLI to automate mechanical steps in the experimentation loop, such as launching experiments and collecting metrics. For inference, Doppel migrated from a GCP Cloud Run stack to Modal, reducing model build times by up to 10x through image layer caching and persistent volumes. Modal's serverless architecture automatically scales inference workloads to handle traffic spikes, and it eliminates the need for custom HTTP service layers around models, simplifying deployment and reducing integration code.