Welcome to the Demo Code Repository for MLFlow

To install the mlflow conda environment in the project folder, inside your terminal navigate to the project folder and then run the conda command:

conda env create -f mlflow_env.yaml

This command will install environment with name mlflow and then you can activate the environment by using command:

conda activate mlflow

Common Gotchas while running MLFlow in Jupyter environment:

Not running the logging code in the same cell in the notebook.
Partial logging or logging parameters before the model training.
Not running the server before hand and running the logging code inside your notebook.
Writing the parameter names by hand in a complex sklearn pipeline or column transformer object with multiple sub-parameters.

Will add more....

Thanks to all who have joined my sessions and attending them with great enthusiasm 👏👍

Thank you all for encouraging and positive feedback. I will work on the feedback and try to improve in the upcoming sessions 🙏😊

Name		Name	Last commit message	Last commit date
Latest commit History 6 Commits
data		data
mlartifacts		mlartifacts
mlruns		mlruns
.gitignore		.gitignore
HP Tuning through Hyperopt.ipynb		HP Tuning through Hyperopt.ipynb
Hyperopt.ipynb		Hyperopt.ipynb
MLFlow Demo.ipynb		MLFlow Demo.ipynb
MLFlow Grid Search.ipynb		MLFlow Grid Search.ipynb
MLFlow Mentos Zindagi.ipynb		MLFlow Mentos Zindagi.ipynb
MLFlow with Data Logging.ipynb		MLFlow with Data Logging.ipynb
README.md		README.md
mlflow_env.yaml		mlflow_env.yaml