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When a data Scientist or ml engineer develop a ML model for solving businesses related problems they create a model but sometimes need to retrain your model because more type of data are generated in a every single second.
The Tools for automate your workflows.
- GitHub Actions
- Jenkins
- Bitbucket
- Grafava (Advance level)
- CML (Continues Machine Learning)
and many more these are the best.
For you ML model GitHub Actions is best Tool for automate your workflows
Create a small project and push in your GitHub.
Then go to Actions tab.
Select a workflows according to your project suppose i created a ML model i selected a python workflows. You can also write a custom workflows just directly write a CML.yaml file click here for instructions.
If you are using GitHub Actions no manual writing is required just select a workflow.
Above below On after runs: be sure that you run install dependencies file.
like : pip install -r requirments.txt Then step by step execute your file like this :
- Python main.py # suppose in this file you collect a data from online and export to csv.
Then you need to train your model.
Python train.py #after running this file have some accuracy write in any file your accuracy.
Automate your workflows and with any tool you like.
Note : For every push which is in your main or any other branch whenever a push in you repo automatically build start and jobs are in run for checking you can write your new model accuracy.