1. using pandas, numpy to read and preprocess patients' data, and maintain it in a pd.df. including >10000 patients, >100000 blood test data
2. using XGBoost to build a decision tree model, to predict if a patient has AKI from his/her general info and historical blood test data. with >98% accuracy
3. using socket to read real-time blood testdata from simulator, predict simultaneously, and page to the hospital with low latency.
4. build docker image; write unit test, integration test, validation module; run and test automatically.
5. pushed docker image to Azure Kubernetes. add recovery mechanisms (store and reload csv files)
6. use prometheus and alertmanager to generate metrics, send alerts automatically.

• Dockerfile: --local=True

Assume the simulator will run outside of Docker:

python simulator.py

Build Docker image:

docker build -t cw3 .

Set Docker environment variables for running:

docker run -e MLLP_ADDRESS=host.docker.internal:8440 -e PAGER_ADDRESS=host.docker.internal:8441 -p 8000:8000 cw3

View localhost:8000 for metrics.

Login to Azure:

az login

Login to Azure Container Registry (ACR):

az acr login --name 

Build Docker image for Linux x86-64 platform:

docker build --platform=linux/x86-64 -t  .

Push Docker image to ACR:

docker push 

Delete existing Kubernetes deployment:

kubectl delete deployment aki-detection -n 

Apply Kubernetes configuration:

kubectl apply -f kubernetes/deployment.yaml

To see logs:

kubectl logs --namespace= -l app=aki-detection -n 

To check port:

kubectl get pods -n 

To forward Prometheus to local port (then access via http://localhost:8000) or simply access http://172.166.8.31:9090:

kubectl -n  port-forward {port_name} 8000:8000

To deploy alerting rules, first apply the alert rule yaml

kubectl apply -f kubernetes/alerting-rules.yaml

Then restart prometheus server to fetch the newest alerting_rules

kubectl delete deployment prometheus -n 
kubectl apply -f kubernetes/prometheus-deployment.yaml

• This is a Group Project Done by Olivia, Tammy, Iqbal, and Tony. During course 70102 'Software Engineering for Machine Learning Systems' @ ICL
• All resources came from Andrew Eland, DoC, Imperial College London