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This project is a basic introduction to the features of MLflow. It's designed to help me understand how MLflow works and how it can be used in machine learning projects.
In this project, I've set up a simple machine learning workflow to explore the features of MLflow. The goal wasn't to follow a rigorous data science workflow, but rather to get things running and see how it goes.
Here are some of the MLflow features I've explored in this project:
Tracking: I've used MLflow's tracking feature to log parameters, metrics, and artifacts. This helped me understand how MLflow can be used to keep track of different experiments in a systematic way.
Projects: I've set up this project as an MLflow project, which helped me understand how MLflow can be used to package machine learning code in a reusable and reproducible way.
Models: I've used MLflow's model feature to save and load models. This helped me understand how MLflow can be used to manage models and their versions.
To run this project, you need to have Python and MLflow installed on your machine. Once you have these prerequisites, you can follow these steps:
python main.py
mlflow ui
http://localhost:5000
to view the MLflow UI and see the results of your script.Please note that this project is a basic introduction to MLflow and does not follow a rigorous data science workflow.
I plan to explore more advanced features of MLflow and use it in more complex machine learning projects. I also plan to follow a more rigorous data science workflow in my future projects.
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