Using On-Demand GPUs to Build and Deploy ML Models with Hex and Modelbit

The text discusses how to use Modelbit alongside Hex to leverage scalable compute with on-demand GPUs for model training and deployment. It provides a step-by-step guide on building, training, and deploying a Grounding DINO computer vision model using Python packages in the Hex platform. The process involves setting up the model configuration and checkpoint, importing necessary methods from GroundingDINO, wrapping the core line of code in a function to be executed remotely by Modelbit on GPU-enabled compute resources, pulling the loaded model back down for use in code, writing an inference function, deploying the model using Modelbit's REST API, and finally integrating the REST API into the Hex notebook for further processing.