🚀 MLOps Project 📌 Overview

This project demonstrates an end-to-end MLOps pipeline covering data ingestion, model training, experiment tracking, model versioning, deployment, and monitoring. It is designed to follow best practices for reproducible, scalable, and production-ready machine learning systems.

🏗️ Project Architecture ├── data/ # Raw and processed datasets ├── notebooks/ # Exploratory analysis & experiments ├── src/ # Source code (training, inference, utils) ├── models/ # Saved / versioned models ├── pipelines/ # Training & inference pipelines ├── app.py # Application (API / Streamlit / UI) ├── requirements.txt # Python dependencies ├── Dockerfile # Containerization ├── mlruns/ # MLflow experiment tracking └── README.md # Project documentation

⚙️ Tech Stack

Programming Language: Python

ML Framework: Scikit-learn / XGBoost / PyTorch / TensorFlow

Experiment Tracking: MLflow

Data Versioning: DVC / Git

Model Registry: MLflow Model Registry

Containerization: Docker

CI/CD: GitHub Actions / GitLab CI

Deployment: Streamlit / FastAPI / Flask

Monitoring: Prometheus / Evidently / Custom Metrics

📊 Workflow

Data Ingestion

Load data from local storage or external sources

Perform data validation and preprocessing

Model Training

Train ML models using configurable parameters

Perform hyperparameter tuning

Track experiments with MLflow

Model Evaluation

Evaluate model performance using standard metrics

Compare experiments and select the best model

Model Versioning

Register models in MLflow Model Registry

Promote models to staging/production

Deployment

Serve the model using an API or UI

Containerize using Docker

Monitoring

Track model performance and data drift

Log predictions and metrics