Topic: "end-to-end-ml-workflows"
kletobias/advanced-mlops-lifecycle-hydra-mlflow-optuna-dvc
End-to-end MLOps pipeline showcasing senior-level best practices with Hydra for configuration, MLflow for experiment tracking, Optuna for hyperparameter tuning, and DVC for data/version control. This repository focuses on reproducibility, modular design, and streamlined collaboration—an ideal demonstration of advanced MLOps capabilities.
Language: Python - Size: 680 KB - Last synced at: 8 days ago - Pushed at: 8 days ago - Stars: 1 - Forks: 0
abhipatel35/Automated-Machine-Learning-Pipeline-for-Iris-Dataset-Classification
Automated ML pipeline for Iris dataset classification using Decision Tree. Features PCA dimensionality reduction and standard scaling.
Language: Python - Size: 6.84 KB - Last synced at: 2 months ago - Pushed at: about 1 year ago - Stars: 1 - Forks: 0
Abishek7952/resume-classifier
An end-to-end machine learning web app that classifies PDF resumes into job-fit categories. Built with FastAPI, Streamlit & Docker. Deployed on Render.
Language: Python - Size: 165 MB - Last synced at: 8 days ago - Pushed at: 12 days ago - Stars: 0 - Forks: 0
Karthi-DStech/End-to-End-MLOps-Training-for-Insurance-Claims
The "Insurance Claims MLOps Lifecycle Automated Pipeline" GitHub project offers an efficient solution for insurance claim processing. Leveraging Azure services, it covers data engineering, model development, MLOps integration, deployment, and application. Automated pipelines streamline workflows, ensuring robust, scalable production environments.
Language: Python - Size: 77.1 KB - Last synced at: 9 months ago - Pushed at: 9 months ago - Stars: 0 - Forks: 0
