updated image

readme.md

@@ -1,62 +1,3 @@
-# Sandbox to implement MLOps practices
+# End-to-End MLOps
 
-![Systemarchitektur](src/system_architecture.jpg)
-
-# THIS IS NOT UP TO DATE!
-
-## Service overview
-
-The environment consists of the following services: *test
-
-- Mlflow, port 5003, manage experiments and models
-- Grafana, port 3000, visualize inference data
-- Minio, port 9000/9001, s3 object storage used by mlflow and dvc
-- Mysql, port 3306, sql db used by mlflow
-- Inference, port 5001, inference api to run model
-
-## How to setup, start and stop the services
-
-The services are hosted as docker containers using docker-compose.
-
-- Make sure docker is up and running on your system
-- Setup and start the services using `make setup_and_start_services`
-- Stop the services using `make stop_services`
-- Start the services using `make start_services
-
-## How to backup and restore the docker volumes`
-
-The service data is stored in volumes.
-
-- Run `make backup_docker_volumes` to create a local backup in the folder `backup`
-- Run `make restore_docker_volumes` to restore the volume from the backups
-
-## How to manage datasets
-
-- Datasets are stored in the folder datasets. THe version control is handled by dvc. The datasets will be synced to github, but stored at another remote (default: s3)
-- To download the dataset files, run `make dvc_pull_s3`
-- To upload a new dataset file or sync changes, run `make dvc_comit_push_s3` and commit changes to github
-
-## How to deploy a model
-
-- Your github token must be configured to allow repo and workflow access
-- Run `make start_dev_deployment_workflow`to start the workflow (no protection rules)
-- Run `make start_prod_deployment_workflow`to start the workflow (approval required, deployment is only allowed from `main`)
-- Check the workflow status in github actions
-
-### How to configure a new environment
-
-- Open the environment configuration
-- Click `New Environment`
-- Enter a name (lower key preferred)
-- Configure potection rules
-- Set the environment secrets `AWS_ACCESS_KEY_ID` and `AWS_SECRET_ACCESS_KEY`
-- Set the environment variables `ÀWS_DEFAULT_REGION` and `BUCKET_NAME` to configure the S3 connection
-
-## Next development steps
-
-- Save more artifacts
-- Advanced model training code with multiple parameters
-- Deploy to an automated testing environment and run tests there
-- Deploy to target system
-- Export the model
-- Run tests with the compiles model and the implementation code
+![Systemarchitektur](src/system_architecture.pdf)