• This project is designed to showcase the integration of two powerful open-source platforms, MLflow and Prefect, for managing the machine learning lifecycle.

• The project will utilize a pre-existing machine learning model XGboost and use MLflow to track and manage the model's performance and parameters as it is trained and deployed.

• Prefect will be used to manage the overall workflow orchestration , including scheduling, monitoring and alerting.

• The goal of this project is to provide a practical example of how these two platforms can be used together to streamline the machine learning development process and improve the efficiency of the entire workflow.

Additionally, the project will demonstrate how to use MLflow to keep track of the model’s performance and Prefect to automate the process locally.