Hello Recruiter, let me walk you through an MLOps Automation project called the Automated Model Retraining System.

The objective of this project is to automatically retrain machine learning models whenever new data becomes available or model performance starts degrading.

Instead of manually retraining models, the entire process is automated using Kubernetes and Argo Workflows.

This project demonstrates:

• MLOps
• Workflow Automation
• Kubernetes
• CI/CD Concepts
• Production ML Operations
• Cloud Native Architecture

Many organizations use machine learning models in production.

Over time:

• Customer behavior changes
• Market conditions change
• Data patterns change

As a result, model accuracy gradually decreases.

This problem is called:

Model Drift

Without retraining:

• Prediction quality drops
• Business decisions become less accurate
• Customer experience can suffer

The goal of this solution is to automatically retrain and redeploy models with minimal manual effort.

From a TPM perspective, my responsibilities would include:

• Managing end-to-end delivery
• Coordinating Data Science, Development, QA, and DevOps teams
• Planning releases
• Managing risks and dependencies
• Tracking sprint execution
• Monitoring deployment readiness
• Communicating with stakeholders

My role is to ensure the solution is delivered successfully and operates reliably in production.

The workflow looks like this:

New Data Available

↓

Argo Workflow Trigger

↓

Data Validation

↓

Model Training

↓

Model Evaluation

↓

Performance Validation

↓

Container Build

↓

Kubernetes Deployment

↓

Production Monitoring

Everything is automated through workflow orchestration.

Machine Learning

• Python
• Scikit-Learn

Workflow Orchestration

• Argo Workflows

Containerization

• Docker

Container Platform

• Kubernetes

Version Control

• GitHub

Cloud Platform

• AWS

Monitoring

• Prometheus
• Grafana

Project Management

• Jira
• Confluence

Argo Workflows helps automate complex processes inside Kubernetes.

Instead of manually running multiple steps:

• Data validation
• Training
• Testing
• Deployment

Argo executes them automatically in the correct sequence.

Benefits:

• Reduced manual effort
• Better reliability
• Faster execution
• Improved consistency

Let me explain the workflow step by step.

Step 1

New training data becomes available.

↓

Step 2

Argo Workflow gets triggered.

↓

Step 3

Data quality checks are performed.

↓

Step 4

Machine learning model is retrained.

↓

Step 5

Model performance is evaluated.

↓

Step 6

If accuracy meets the threshold:

Deployment proceeds.

If not:

Deployment is stopped.

↓

Step 7

Docker image is created.

↓

Step 8

Application is deployed to Kubernetes.

↓

Step 9

Monitoring begins.

This creates a fully automated retraining pipeline.

The application runs on Kubernetes.

Benefits include:

• High availability
• Auto-scaling
• Self-healing
• Automated deployments

If a container fails:

Kubernetes automatically replaces it.

This improves production stability.

This project follows automation principles similar to CI/CD.

Workflow:

Code Change

↓

Git Repository

↓

Build Validation

↓

Model Training

↓

Testing

↓

Deployment

↓

Monitoring

Benefits:

• Faster releases
• Reduced operational effort
• Improved consistency
• Lower deployment risk

Monitoring is critical in production ML systems.

This project uses:

For:

• Metrics collection
• Infrastructure monitoring
• Workflow monitoring

For:

• Dashboards
• Trend analysis
• Performance visibility

Important Metrics:

• Model accuracy
• Training success rate
• Deployment success rate
• Resource utilization
• Workflow execution time

As a TPM, I would manage risks such as:

• Failed retraining jobs
• Poor model accuracy
• Deployment failures
• Infrastructure outages
• Capacity limitations

Mitigation Strategies:

• Validation checkpoints
• Automated rollback plans
• Monitoring alerts
• Production health checks
• Approval gates for deployment

Example sprint plan:

Sprint 1

• Requirements gathering
• Architecture design

Sprint 2

• Data pipeline setup

Sprint 3

• Model training automation

Sprint 4

• Argo Workflow implementation

Sprint 5

• Kubernetes deployment

Sprint 6

• Monitoring setup

Sprint 7

• UAT and production release

Throughout delivery:

• Daily standups
• Sprint reviews
• Retrospectives
• Stakeholder demos

The solution provides:

• Reduced manual retraining effort
• Faster model updates
• Improved prediction quality
• Better operational efficiency
• Reduced deployment risk
• Improved scalability

As business grows:

• Additional Kubernetes pods can be added.
• Multiple workflows can run in parallel.
• Infrastructure can scale automatically.
• Retraining frequency can increase without major operational effort.

This ensures long-term scalability.

This project demonstrates:

• MLOps automation
• Kubernetes orchestration
• Argo Workflow automation
• CI/CD concepts
• Production monitoring
• Cloud-native deployment

From a Technical Project Manager perspective, it highlights:

• Delivery ownership
• Agile project execution
• Risk management
• Cross-functional coordination
• Cloud and DevOps understanding
• AI and MLOps awareness

The key value of this solution is that it transforms a manual machine learning process into a fully automated and scalable production workflow.

Thank you. I would be happy to answer any questions regarding the architecture, workflow automation, Kubernetes deployment, MLOps lifecycle, or project delivery approach.

===============================================

A fully automated, production-grade MLOps system that retrains ML models whenever new data arrives—using Kubernetes-native orchestration, experiment tracking, CI/CD, and real-time serving.

This pipeline automates the entire ML lifecycle:

• Tracks experiments with MLflow
• Promotes best models to registry
• Serves models via FastAPI
• Retrains automatically when new data arrives in MinIO
• Deploys updates via GitHub Actions + ArgoCD

Architecture

End-to-end flow: MinIO → Argo Events → Argo Workflows → MLflow → FastAPI → GitHub Actions → Kubernetes

• Setup: Python venv, MLflow, scikit-learn, pandas
• MLflow UI hosted locally/EC2
• Logs parameters, metrics, artifacts

• Best model promoted to Production stage
• FastAPI inference service pulls latest production model
• Dockerized FastAPI app

• Kubernetes setup via Minikube
• DAG: data ingestion → training → logging → registry update
• Argo Events detect new data in MinIO → trigger retraining
• CI/CD: GitHub Actions build Docker image + update manifests

Argo Workflow

DAG execution of training pipeline triggered by new data.

Argo Logs

Logs show MLflow warnings, training completion, and run URL.

GitHub Actions

Automated build + manifest update triggered by code push.

# Step 1: Deploy MinIO + MLflow + FastAPI
kubectl apply -f minio-deployment.yaml
kubectl apply -f minio-service.yaml
kubectl apply -f mlflow-deployment.yaml
kubectl apply -f fastapi-deployment.yaml
# Step 2: Upload new data to MinIO
mc alias set minio http://localhost:9000 minio minio123
mc cp ./data/new_data.csv minio/input-data/
# Step 3: Trigger Argo Events + Workflow
kubectl apply -f minio-event-source.yaml
kubectl apply -f minio-sensor.yaml
kubectl apply -f train-workflow.yaml
# Step 4: View MLflow UI
http://<EC2-IP>:5000
# Step 5: CI/CD (auto triggered via GitHub Actions)