MLOpsE2EClassificationTermProject

This project implements a complete MLOps pipeline for a U.S. Visa Approval Classification System, covering all essential components — from data ingestion to deployment and monitoring. The goal is to predict whether a visa application will be approved or denied, using machine learning and production-grade MLOps tools.

The application is deployed on AWS EC2 using Docker containers, with the image stored and pulled directly from AWS Elastic Container Registry (ECR) through an automated GitHub Actions CI/CD pipeline.

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📘 Overview

This project demonstrates:

• Data ingestion & transformation
• Model training & hyperparameter optimization
• Model registry and versioning with AWS S3
• FastAPI deployment
• Continuous evaluation with Evidently AI

It is designed following end-to-end MLOps best practices, ensuring scalability, reproducibility, and maintainability.

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⚙️ Tech Stack

Category	Tools / Libraries
Data Processing	pandas, numpy, matplotlib, seaborn, plotly
ML Modeling	scikit-learn, xgboost, catboost, imblearn, scipy
MLOps & Monitoring	dill, PyYAML, neuro_mf, boto3, botocore, mypy-boto3-s3, evidently==0.2.8
Database	pymongo
Backend/API	fastapi, uvicorn, jinja2, python-multipart
Utilities	from_root, certifi, dnspython

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📂 Project Structure

MLOpsE2EClassificationTermProject/
│
├── data/                        # Raw & processed data
├── notebooks/                   # Exploratory analysis notebooks
├── src/
│   ├── components/              # Data ingestion, transformation, training modules
│   ├── pipeline/                # Training & prediction pipelines
│   ├── utils/                   # Helper functions
│   ├── logger.py                # Custom logging
│   ├── exception.py             # Error handling
│
├── app.py                       # FastAPI main application
├── template.py                  # Folder structure generator
├── requirements.txt
├── setup.py
└── README.md

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🧩 Installation

1️⃣ Create and activate conda environment

conda create -n visa python=3.8 -y
conda activate visa

2️⃣ Install dependencies

pip install -r requirements.txt

3️⃣ (If MongoDB error occurs)

pip uninstall -y pymongo motor mongoengine djongo
pip install -U "pymongo>=4.7" dnspython certifi

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🧠 Features

🧮 Data Preprocessing

• Handles missing values and outliers
• Encodes categorical variables
• Normalizes numeric features

🧠 Model Training

• Trains multiple models (XGBoost, CatBoost, RandomForest, etc.)
• Uses GridSearchCV for parameter optimization
• Saves model artifacts with dill

☁️ Model Versioning & Storage

• Stores trained models and metadata in AWS S3
• Uses boto3 and neuro_mf for version tracking

⚡ Deployment via FastAPI

• REST API endpoint for prediction: /predict
• Web UI using Jinja2 templates
• Deployed using Uvicorn

📊 Continuous Monitoring

• Integrated with Evidently AI (v0.2.8) for drift detection
• Tracks model performance and feature drift over time

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🚀 Usage

🧪 Run training pipeline

python src/pipeline/training_pipeline.py

⚙️ Start API server

uvicorn app:app --reload

📈 Generate Evidently report

python src/components/data_monitoring.py

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🧾 Example API Request

POST /predict

{
  "case_id": "A12345",
  "country_of_origin": "India",
  "education_level": "Masters",
  "job_experience": 5,
  "employer_size": 200,
  "prev_visa_denials": 0
}

Response:

{
  "prediction": "Approved",
  "probability": 0.89
}

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☁️ AWS Integration

Environment Variables

Create a .env file in the root directory:

AWS_ACCESS_KEY_ID=<your_aws_key>
AWS_SECRET_ACCESS_KEY=<your_secret_key>
MONGODB_CLUSTER_URI=<your_mongo_connection_string>
BUCKET_NAME=<your_s3_bucket_name>
AWS_DEFAULT_REGION=<your_aws_region>
ECR_REPO=<your_ecr_url>

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🧹 Troubleshooting

If you face MongoDB issues:

pip uninstall -y pymongo motor mongoengine djongo
pip install -U "pymongo>=4.7" dnspython certifi

If S3 upload fails:

• Check your AWS credentials
• Verify IAM role permissions
• Ensure correct bucket region

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📊 MLOps Pipeline (Flow)

    A[Data Ingestion] --> B[Data Transformation]
    B --> C[Model Training]
    C --> D[Model Evaluation]
    D --> E[Model Storage (AWS S3)]
    E --> F[FastAPI Deployment]
    F --> G[Prediction API]
    G --> H[Monitoring (Evidently AI)]
    H --> A

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📦 Requirements Summary

pandas
numpy
matplotlib
plotly
seaborn
scipy
scikit-learn
imblearn
xgboost
catboost
pymongo
from_root
evidently==0.2.8
dill
PyYAML
neuro_mf
boto3
mypy-boto3-s3
botocore
fastapi
uvicorn
jinja2
python-multipart
-e .

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👨‍💻 Author

Pankaj Kumar Pramanik Data, AI & MLOps Engineer 🌐 pankajpramanik.com