MLOps with model deployment using docker and scaling using kubernetes; API deployment with FastAPI; using MLFlow for model tracking for water potability dataset
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Updated
Aug 30, 2026 - Python
MLOps with model deployment using docker and scaling using kubernetes; API deployment with FastAPI; using MLFlow for model tracking for water potability dataset
The project demonstrates a CNN image classifier model "cheetah vs hyena predictor" deployed via a web interface.
Flask App for machine learning predictions with embedded dash app
Machine learning model deployment from notebook to production using FastAPI, Docker, and CI/CD pipelines.
End-to-end machine learning projects involve the complete process of developing a machine learning model, starting from data collection and preprocessing to training, evaluation, and deployment. These projects encompass data exploration, feature engineering, model selection, performance evaluation, and integration with production systems
🚗 Car Dheko - Used Car Price Prediction This project enhances Car Dheko's customer experience by deploying an ML model that predicts used car prices accurately. Using a multi-city dataset, we perform data cleaning, feature engineering, and model optimization. The final model is hosted on a Streamlit app, providing instant price prediction.
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