Deploy a custom model on Replicate and expose it as a production API deployment with auto-scaling

Steps

1. Package your model using Cog (Replicate's containerization tool): define predictor.py with a Predict class implementing predict(), and a cog.yaml specifying the base image and Python dependencies
2. Build and push the Cog model to Replicate with cog push, which creates a new model version on the platform
3. Create a Deployment using the POST /v1/deployments API endpoint or the Replicate dashboard, specifying the model, version, hardware type, min_instances, and max_instances for auto-scaling
4. The deployment provides a dedicated URL distinct from the shared model endpoint; reference the deployment name in API calls rather than the model version directly
5. Invoke the deployment via POST /v1/predictions, setting the version field to the deployment's model version; the deployment auto-scales between min and max instances based on queue depth
6. Monitor deployment metrics (request volume, latency, instance utilization, error rates) from the Replicate dashboard and adjust min/max instance counts as traffic patterns evolve