How Doppel eliminated ML infrastructure tax with Modal | Modal Blog

What this post added

This post details how Doppel migrated their ML training and inference workflows to Modal, significantly reducing ML infrastructure tax. For training, it highlights the use of Modal's `map()` function to parallelize K-fold cross-validation and other experiments, shortening feedback loops. For inference, it describes how Modal simplifies deployment by removing the need for manual HTTP service layers, reducing build times through image layer caching and persistent volumes, and automating scaling for traffic spikes. The post contrasts the previous GCP inference stack with the Modal approach, emphasizing faster iteration and reduced operational overhead.