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how-to-guides/use-service/use-service.md
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@ -51,7 +51,8 @@ Some configuration options are also supported.
```sh
great-ai greeter.py --port 8000 --host 127.0.0.1 --timeout_keep_alive 10
```
> For more options (but no Notebook support, use [uvicorn](https://www.uvicorn.org/){ target=_blank })
??? note "More options"
For more options (but no Notebook support), simply use [uvicorn](https://www.uvicorn.org/){ target=_blank } for starting your app (available at `greeter.app`).
### In production
@ -80,7 +81,7 @@ docker run -p 6060:6060 --volume `pwd`:/app --rm \
#### Use a Platform-as-a-Service
Similarly to the previous approach, your code will run in a container. However, instead of manually managing it, you can just choose from a plethora of PaaS providers (such as [DO App platform](https://www.digitalocean.com/products/app-platform){ target=_blank } or [MLEM](https://mlem.ai/){ target=_blank }) that take a Docker image as a source and handle the rest of the deployment.
Similarly to the previous approach, your code will run in a container. However, instead of manually managing it, you can just choose from a plethora of PaaS providers (such as [AWS ECS](https://aws.amazon.com/ecs/){ target=_blank }, [DO App platform](https://www.digitalocean.com/products/app-platform){ target=_blank }, [MLEM](https://mlem.ai/){ target=_blank }) that take a Docker image as a source and handle the rest of the deployment.
To this end, you can also create a custom Docker image. It is especially useful if you have third-party dependencies, such as [PyTorch](https://pytorch.org/){ target=_blank } or [TensorFlow](https://www.tensorflow.org/){ target=_blank }.