chore: remove aws deployment

use_case_examples/deployment/breast_cancer/README.md

@@ -10,11 +10,9 @@ One can also run this example locally using Docker, or just by running the scrip
 
 1. To train your model you can use `train.py`, or `train_with_docker.sh` to use Docker (recommended way).
    This will train a model and [serialize the FHE circuit](../../../docs/guides/client_server.md) in a new folder called `./dev`.
-1. Once that's done you can use the script provided in Concrete ML in `use_case_examples/deployment/server/`, either use `deploy_to_aws.py` or `deploy_to_docker.py` according to your need.
+1. Once that's done you can use the script provided in Concrete ML in `use_case_examples/deployment/server/`, use `deploy_to_docker.py`.
 
 - `python use_case_examples/deployment/server/deploy_to_docker.py --path-to-model ./dev`
-- `python use_case_examples/deployment/server/deploy_to_aws.py --path-to-model ./dev`
-  this will create and run a Docker container or an AWS EC2 instance.
 
 3. Once that's done you can launch the `build_docker_client_image.py` script to build a client Docker image.
 1. You can then run the client by using the `client.sh` script. This will run the container in interactive mode.

use_case_examples/deployment/cifar/README.md

@@ -12,11 +12,9 @@ One can also run this example locally using Docker, or just by running the scrip
 Deployment this model on your personal machine is not recommended as running a VGG in FHE is computationally intensive. It is recommended to run this on a `m6i.metal` instance from AWS.
 
 1. To compile your model you can use `compile.py`, or `compile_with_docker.py` to use Docker. This will compile the model to an FHE circuit and [serialize it](../../../docs/guides/client_server.md). This will result in a new folder called `./dev`.
-1. Once that's done you can use the script provided in Concrete ML in `use_case_examples/deployment/server/`, either use `deploy_to_aws.py` or `deploy_to_docker.py` according to your need.
+1. Once that's done you can use the script provided in Concrete ML in `use_case_examples/deployment/server/`, use `deploy_to_docker.py`.
 
 - `python use_case_examples/deployment/server/deploy_to_docker.py`
-- `python use_case_examples/deployment/server/deploy_to_aws.py --instance-type m6i.metal`
-  this will create and run a Docker container or an AWS EC2 instance.
 
 3. Once that's done you can launch the `build_docker_client_image.py` script to build a client Docker image.
 1. You can then run the client by using the `client.sh` script. This will run the container in interactive mode.

use_case_examples/deployment/sentiment_analysis/README.md

@@ -12,11 +12,9 @@ One can also run this example locally using Docker, or just by running the scrip
 1. To train your model you can use `train.py`, or `train_with_docker.sh` to use Docker (recommended). This operation might take some time.
    This will train a model and [serialize the FHE circuit](../../../docs/guides/client_server.md).
    This will result in a new folder called `./dev`.
-1. Once that's done you can use the script provided in Concrete ML in `src/concrete/ml/deployment/`, either use `deploy_to_aws.py` or `deploy_to_docker.py` according to your need.
+1. Once that's done you can use the script provided in Concrete ML in `src/concrete/ml/deployment/`, use `deploy_to_docker.py`.
 
 - `python use_case_examples/deployment/server/deploy_to_docker.py`
-- `python use_case_examples/deployment/server/deploy_to_aws.py`
-  this will create and run a Docker container or an AWS EC2 instance.
 
 3. Once that's done you can launch the `build_docker_client_image.sh` script to build a client Docker image.
 1. You can then run the client by using the `client.sh` script. This will run the container in interactive mode.