Open m-mohr opened 3 years ago
We will start experimenting with the training of a random forest model, this model can then indeed be used for inference. A fully generic ML API is out of scope. So something like: ml_features.train_model(type='randomforest').execute_batch(out_format="ml_model_format")
ml_features will need to be some kind of datacube containing features and their labels. Not sure if this can still be a regular datacube.
A fully generic ML API is out of scope.
Just to make this clear to externals: It is out of scope for the openEO Platform project, not necessarily out of scope for the openEO API.
ml_features will need to be some kind of datacube containing features and their labels. Not sure if this can still be a regular datacube.
I'm not so much into the ML terminology. To clarify: Features as in vector features? Which would lead us to vector data cubes?
The API shall provide core functionalities and operators for training, parameterization, prediction and validation of Machine Learning models. (req. 48)
Could be an endpoint /models for pre-trained models and mostly all other things are hopefully possible with dedicated processes?