ML Model Deployment to Databases

Hex has evolved its ML capabilities by enabling seamless deployment of trained models to data warehouses like Snowflake. This allows for batch predictions directly within the database using simple SQL commands, eliminating the need for separate infrastructure or complex deployment pipelines. This integration leverages tools like Modelbit to bridge the gap between model training in Hex and production inference at scale. This post details using on-demand GPUs with Hex and Modelbit to build and deploy ML models, specifically demonstrating the process with a Grounding DINO model. It covers setting up the environment, building the model on remote GPU compute via Modelbit's job execution, and deploying the trained model as a REST API for inference, which is then called back into Hex.