Comments (5)
https://medium.com/analytics-vidhya/fundamentals-of-mlops-part-3-ml-experimentation-using-pycaret-747f14e4c28d
https://medium.com/analytics-vidhya/fundamentals-of-mlops-part-4-tracking-with-mlflow-deployment-with-fastapi-61614115436
https://github.com/pycaret/pycaret/blob/master/examples/TimeSeries_Forecasting.ipynb
https://towardsdatascience.com/new-time-series-with-pycaret-4e8ce347556a
https://towardsdatascience.com/easy-mlops-with-pycaret-mlflow-7fbcbf1e38c6
https://towardsdatascience.com/exploring-model-hyperparameter-search-space-with-pycaret-mlflow-b6c630d71723
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ML Production Ready Checklist:
ML_Production_Readiness_Checklist.xlsx
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@sayantikabanik go through this and comment if required
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https://docs.featurestore.org/
if anyone is keen to read more how MLOps feature stores are designed
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We are good here I belive, closing for now
Please create another issue or reopen this
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Related Issues (20)
- Clean the repo HOT 2
- Mid review feedback [FP2] HOT 1
- final review left over parts HOT 2
- Turn the notebook into python script [model] HOT 4
- Update env.yml file HOT 2
- Need the logic you have used for the extraction from Raw format HOT 3
- fix github actions HOT 4
- Dependencies/pickle/code for the best model HOT 8
- Final review Presentation HOT 4
- Model/data/concept drift
- Project selection HOT 5
- deployment (with UI) HOT 1
- Resources [Git] HOT 1
- Mid review HOT 4
- Add FP1 [reviews/feedbacks received from prof] HOT 4
- Mid review [Presentation edit link + details] HOT 13
- Data collection HOT 19
- Task allocation for new folks HOT 4
- kaggle [modelling] HOT 6
- Data Engineering HOT 10
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