🚀 MLOps Monitoring Stack: Flask App + Prometheus + Grafana on Minikube

This project demonstrates how to containerize a Flask application, deploy it on Minikube, and set up a monitoring and visualization stack using Prometheus and Grafana. It’s designed for developers exploring MLOps, DevOps practitioners, or recruiters seeking proof of hands-on Kubernetes, observability, and infrastructure automation.

---

🔧 Tech Stack

• Flask - Lightweight Python web framework
• Docker - Containerization of the app
• Kubernetes (Minikube) - Local K8s cluster for deployment
• Prometheus - Metrics collection and scraping
• Grafana - Visualization dashboards for real-time monitoring
• Helm - Package manager for Kubernetes

---

---

🏁 Getting Started

✅ 1. Run Flask App Locally

You can start with testing the Flask app locally before moving to the Kubernetes environment.

---

🐳 Deploy Flask App on Minikube

🔨 2. Build Docker Image

docker build -t flask-metrics-app:latest .

⚙️ 3. Start Minikube

minikube start
minikube image load flask-metrics-app:latest

📦 4. Deploy App to K8s

kubectl apply -f flask-app.yaml
minikube service flask-metrics-app --url

---

📈 Install Prometheus Using Helm

💾 5. Install Helm

# Windows
winget install Helm.Helm

# Or use:
scoop install helm
choco install kubernetes-helm

📡 6. Deploy Prometheus

helm repo add prometheus-community https://prometheus-community.github.io/helm-charts
helm repo update
helm install prometheus prometheus-community/prometheus --namespace monitoring --create-namespace

🧭 7. Access Prometheus

kubectl port-forward -n monitoring svc/prometheus-server 9090:80
# Visit: http://localhost:9090

---

🔄 Configure Prometheus to Scrape Flask App

🛠️ 8. Add Scrape Target

# kubectl edit configmap prometheus-server -n monitoring

scrape_configs:
  - job_name: 'flask-app'
    static_configs:
      - targets: ['<FLASK_CLUSTER_IP>:5000']

💡 Get Cluster IP:

kubectl get svc

🔁 Restart Prometheus Pod

# Option 1: By Label
kubectl get pods -n monitoring --show-labels
kubectl delete pod -l app.kubernetes.io/name=prometheus -n monitoring

# Option 2: By Deployment
kubectl rollout restart deployment prometheus-server -n monitoring

---

📊 Install & Access Grafana

📥 9. Install Grafana via Helm

helm repo add grafana https://grafana.github.io/helm-charts
helm repo update
helm install grafana grafana/grafana -n monitoring --create-namespace

🔌 10. Port Forward to Access

kubectl port-forward svc/grafana -n monitoring 3000:80
# Visit: http://localhost:3000

🔐 11. Login to Grafana

# Get password (Linux/macOS)
kubectl get secret --namespace monitoring grafana -o jsonpath="{.data.admin-password}" | base64 --decode ; echo

# Windows PowerShell
kubectl get secret --namespace monitoring grafana -o jsonpath="{.data.admin-password}"
[System.Text.Encoding]::UTF8.GetString([System.Convert]::FromBase64String("<base64_password>"))

Username: admin

---

📡 Add Prometheus as a Data Source

➕ 12. Connect Prometheus to Grafana

• Navigate to: Connections > Data Sources > Add data source > Prometheus

• Use this URL: http://prometheus-server.monitoring.svc.cluster.local:80

• Click Save & Test

---

📈 Create Custom Dashboards

🧩 13. Create Your First Dashboard

• Navigate to: Dashboard > New Dashboard > Add New Panel

• Add your metric query like:

  total_api_requests_total
  

---

✅ Final Result

You now have:

• A Flask app exposing custom metrics ✅
• Prometheus scraping those metrics ✅
• Grafana visualizing them beautifully ✅
• Everything running on minikube(K8s) ✅

---

💡 Use-Cases

• Monitor ML model inference metrics
• Track API usage over time
• Analyze system health in real-time
• Build an end-to-end reproducible MLOps workflow

---

📝 License

This project is licensed under the MIT License.

---

⭐ Star This Repo

If you found this useful or learned something new, consider giving it a ⭐ on GitHub. It helps others find it too!