Describe where your model will run and what your application needs. We propose model fine-tuning and inference optimization for that setup, with a deployment-ready package configured for your infrastructure.
Start with the stack you already use: your server or cloud environment, available CPU or GPU, application, and expected image volume. You do not need to choose an AI runtime yourself. These requirements shape a deployment proposal, while the inference playground lets you review what the model predicts.
Your model
Inference runtime
Target configuration
An optimized model running
on your agreed infrastructure.
Fine-tuning adapts the model to your images and task. Inference optimization adapts how that model runs on your target hardware. We propose a configuration around the quality, response time, throughput, and memory your workflow needs, then evaluate the tradeoffs on representative inputs.
Fine-tuning scoped to your labeled examples and task.
The delivery brings the model, inference runtime, and target configuration together in a deployment-ready package. Your developer or technical partner can run it on the agreed infrastructure and connect your application to its prediction API, without assembling the model-serving environment from scratch.
A model and inference runtime packaged for the agreed target.
Deployment & inference is part of the connected workflow: prepare, annotate, select, train, and deploy. Your team can revisit an earlier step when evaluation reveals something new. The product illustrations explain the workflow without uploading a dataset.