Not convinced by the example. I don’t see how you can’t use standard scikit-learn for it.
First, the example doesn’t take advantage of sklearn’s built in, super simple parallelization via n_jobs
Then, the entire example could be better wrapped with sklearn’s own cross_validate() which gives you the same functionality: a table of results across experiments.
If you use a different estimator, you can easily concatenate the results into a single df
The rest is the same.
Why you need SQLite for this? (SQLite is great of course for the right use cases)
And if you're doing many orders more experiments (1000s instead of 10s) then that’s probably where MLflow is good (haven’t actually used MLflow)
First, the example doesn’t take advantage of sklearn’s built in, super simple parallelization via n_jobs
Then, the entire example could be better wrapped with sklearn’s own cross_validate() which gives you the same functionality: a table of results across experiments.
If you use a different estimator, you can easily concatenate the results into a single df
The rest is the same.
Why you need SQLite for this? (SQLite is great of course for the right use cases)
And if you're doing many orders more experiments (1000s instead of 10s) then that’s probably where MLflow is good (haven’t actually used MLflow)