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machine-learning-model-deployment

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MLOps with model deployment using docker and scaling using kubernetes; API deployment with FastAPI; using MLFlow for model tracking for water potability dataset

  • Updated Aug 30, 2026
  • Python

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

  • Updated Feb 25, 2025
  • Jupyter Notebook

🚗 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.

  • Updated Nov 4, 2024
  • Jupyter Notebook

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