If the available Inference Engines don’t meet your requirements, you can deploy your own custom solution as a Docker container and run
it on Inference Endpoints. You can use public images like tensorflow/serving:2.7.3 or private images hosted on
Docker Hub, AWS ECR,
Azure ACR, or Google GCR.

The creation flow of your image artifacts from a custom image is the same as the base image. This means Inference Endpoints will create a unique image artifact derived from your provided image, including all model artifacts.
The model artifacts (weights) are stored under /repository. For example, if you use tensorflow/serving as your custom image,
then you have to set `model_base_path=“/repository”:
tensorflow_model_server \
--rest_api_port=5000 \
--model_name=my_model \
--model_base_path="/repository"