docs: removed legace s3 sync

Makefile

@@ -18,15 +18,6 @@ remove_mlflow_ui:
 	@docker rm mlflow_ui || true
 	@docker rmi mlflow_ui_s3 || true
 
-download_datasets:
-	@aws s3 sync s3://${BUCKET_NAME}/datasets datasets --exact-timestamps
-
-download_dataset:
-	@aws s3 sync s3://${BUCKET_NAME}/datasets/$(NAME) datasets/$(NAME) --exact-timestamps
-
-upload_datasets:
-	@aws s3 sync datasets s3://${BUCKET_NAME}/datasets --exact-timestamps
-
 start_prod_deployment_workflow:
 	curl -X POST \
 		-H "Accept: application/vnd.github+json" \

readme.md

@@ -43,11 +43,3 @@
 - Deploy to target system
 - Export the model
 - Run tests with the compiles model and the implementation code
-
-## How to manage raw datasets with aws cli (legacy)
-
-- Datasets are stored in the folder `datasets`, which is not synced with github, but stored in s3 instead
-- To download all existing datasets, use `make download_datasets`
-- To download a specific dataset use `make download_dataset NAME=<dataset_name>`
-- To upload a new dataset to s3, add it to the `datasets`folder and use `make upload_datasets`
-- This feature is only for storing raw data. Procecced datasets are stored as artifacts and can be accessed using the mlflow ui