How to track model changes in Django and execute dependent workflows

Framework for managing machine learning models using MLFlow, focusing on advanced model tracking, versioning, and experiment management.

Covers essential features like model tracking, versioning, and experiment, providing a foundation for efficient ML project lifecycle management.

MLflow adapter for CrateDB.

LifeSavAR transforms your smartphone and HoloLens into a lifesaving tool, offering immediate, augmented reality-guided CPR and AED instructions in emergencies. It's your pocket-sized first responder, empowering you to act confidently and effectively when every second counts.

Malware Detection System using various machine learning and deep learning models, incorporating data preprocessing, feature engineering, and model evaluation techniques, while tracking experiments with MLflow for performance comparison and reproducibility.

This project automates the end-to-end machine learning training pipeline using Jenkins, ensuring seamless integration and automation. When new data arrives or code is updated, the pipeline automatically preprocesses the data, trains a machine learning model, evaluates its performance, saves the model with versioning, and logs the entire workflow.