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A fully functional end to end ML application integrating ML flow, Docker and cloud

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Raghucharan16/MLops

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workflow

1.Update config.yaml 2.update schema.yaml (at the time of data validation) 3.update params.yaml (at the time of model training) 4.update entity 5.update configuration manager in scr/config 6.update the components 7.update the pipline 8.update the main.py 9.update the app.py

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A fully functional end to end ML application integrating ML flow, Docker and cloud

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