/MLOps

Primary LanguageJupyter NotebookMIT LicenseMIT

MLOps

End to end ML project

Delivery of the project

Deployement using Azure

How to monitor your project

Tools used :

  • Python

  • Git for managing the project

  • Unit and Integrated testing: tox (Pytest), it will be tested on GitHub action server (CI, Integration)

  • Docker: creating an image for the github server

  • Deliver the project to the Azure repo. (CD, Delivery)

  • Deployement on Azure server (CD, Deployement)

  • Monitoring : Using evidently.ai

  • Airflow for scheduling the continuous training (CT, Training)

  • DVC for Data managment

  • MLflow for Experiment tracking

  • Dags hub for model registry

  • BentoML for