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Integration:  git github
f5d54ada60
folder structure added
1 month ago
a79a66a885
model trainer is done
1 month ago
5151bf7e18
complete the evaluation
1 month ago
src
5151bf7e18
complete the evaluation
1 month ago
f5d54ada60
folder structure added
1 month ago
5151bf7e18
complete the evaluation
1 month ago
92ae6f14f4
Initial commit
1 month ago
5151bf7e18
complete the evaluation
1 month ago
f5d54ada60
folder structure added
1 month ago
5151bf7e18
complete the evaluation
1 month ago
ff73ec06f4
preprae model is done
1 month ago
05da055450
added requirements.txt
1 month ago
5151bf7e18
complete the evaluation
1 month ago
05da055450
added requirements.txt
1 month ago
130c12c17a
data ingestion is completed
1 month ago
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README.md

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End-to-End-Canser-Classification_using_MLFLOW

Workflows

  1. Update config.yaml
  2. Update secrets.yaml [Optional]
  3. Update params.yaml
  4. Update the entity
  5. Update the configuration manager in src config
  6. Update the components
  7. Update the pipeline
  8. Update the main.py "end point"
  9. Update the dvc.yaml

....

Step 02

MLflow

Documentation

MLflow tutorial

cmd

mlflow ui

dagshub

dagshub

MLFLOW_TRACKING_URI=https://dagshub.com/sheriff786/End-to-End-Canser-Classification_using_MLFLOW.mlflow MLFLOW_TRACKING_USERNAME=sheriff786 MLFLOW_TRACKING_PASSWORD=89b16177f0e2435725ac3ad5980f0fd215c37743 python script.py

Run this to export as env variables:


export MLFLOW_TRACKING_URI=https://dagshub.com/sheriff786/End-to-End-Canser-Classification_using_MLFLOW.mlflow

export MLFLOW_TRACKING_USERNAME=sheriff786

export MLFLOW_TRACKING_PASSWORD=89b16177f0e2435725ac3ad5980f0fd215c37743


Tip!

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mlflow

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