One of the reasons this is interesting to me is that we quickly hit one of MLflow's limitations[0] in our machine learning platform[1]. Users collaborate in near real-time on notebooks, schedule long-running notebooks, and we automatically detect user's models and then save them so they don't have to. They can then deploy them in one click. However, MLflow has trouble with models requiring high-dimensional inputs, which is most non toy models I've seen.
The usual "solution" is to write custom wrapping code for this because it only supports 2D DataFrames, which is unacceptable for us because that would mean users would have to do it, so we'll take care of this too.
The usual "solution" is to write custom wrapping code for this because it only supports 2D DataFrames, which is unacceptable for us because that would mean users would have to do it, so we'll take care of this too.
- [0]: https://github.com/mlflow/mlflow/issues/3570
- [1]: https://iko.ai