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Hello,
I just wanted to provide the following to update the README.md file. Since I was unauthorized to make a merge request, thought I would post it here.
Cheers!
(Also included the README.md file as a txt, just in case)
Example MLflow project
Overview
This is an example MLflow project for the MLflow Quickstart
documentation.
Using both the UCI Wine Quality dataset (by P. Cortez, A. Cerdeira, F.
Almeida, T. Matos and J. Reis.) and Elastic Net to predict quality, we create an MLflow project.
Moreover, The example uses MLproject to set up a Conda environment, define parameter types and defaults, entry point for training, etc.
Instructions
If you reached this repository from going through the MLflow Quickstart
documentation, please follow these instructions (else, feel free to independently go through the material yourself):
Hello,
I just wanted to provide the following to update the README.md file. Since I was unauthorized to make a merge request, thought I would post it here.
Cheers!
(Also included the README.md file as a txt, just in case)
Example MLflow project
Overview
This is an example MLflow project for the MLflow Quickstart
documentation.
Using both the UCI Wine Quality dataset (by P. Cortez, A. Cerdeira, F.
Almeida, T. Matos and J. Reis.) and Elastic Net to predict quality, we create an MLflow project.
Moreover, The example uses MLproject to set up a Conda environment, define parameter types and defaults, entry point for training, etc.
Instructions
If you reached this repository from going through the MLflow Quickstart
documentation, please follow these instructions (else, feel free to independently go through the material yourself):
1 ) Per the Running MLflow Projects section, run the following:
mlflow run tutorial -P alpha=0.5
mlflow run [email protected]:mlflow/mlflow-example.git -P alpha=5
2 ) Per the Saving and Serving Models
section, run:
python sklearn_logistic_regression/train.py
Therafter, to serve the scikit-learn model through a REST server, run:
mlflow sklearn serve -r <RUN_ID> model
Now, run
curl -d '[{"x": 1}, {"x": -1}]' -H 'Content-Type: application/json' -X POST localhost:5000/invocations
Congrats, you are done! Please refer back to the MLflow Quickstart documentation or feel free to play around more with MLflow!
Cheers!
README..txt
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