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Hi again, I was using MLflow for a while and switched now to a custom solution inspired by MLflow to save all artefacts into a folder with a unique run_id and and params and metrics as json. This way all params are quickly available and some shiny app can pick up those meta info. Do you think it would be beneficial to add a standardised export function to parsnip or workflows?
Best
The text was updated successfully, but these errors were encountered:
We will start working on a model development tool for organizing, benchmarking, and updating models. I think that what you want would happen during that work.
It's not really the same as MLflow; less focus on deployment (for a while) and more on the practical aspects of chronic model building activities.
Hi again, I was using MLflow for a while and switched now to a custom solution inspired by MLflow to save all artefacts into a folder with a unique run_id and and params and metrics as json. This way all params are quickly available and some shiny app can pick up those meta info. Do you think it would be beneficial to add a standardised export function to parsnip or workflows?
Best
The text was updated successfully, but these errors were encountered: