Hi Julie,

I haven't actually used that feature, but what you described does appear to be the intent of "Context Superclass". From the documentation https://docs.intersystems.com/latest/csp/docbook/DocBook.UI.Page.cls?KEY=EBPLR_process, and https://docs.intersystems.com/latest/csp/docbook/DocBook.UI.Page.cls?KEY=EBPL_editor 

I think you then would set the default values on the superclass's properties. Again, I haven't used this, so I might be steering you in the wrong direction! 

Hi Dmitriy,

We are first getting to the point where we distribute out JDBC driver via Maven, and then we will get it integrated into DBeaver. I am not sure what the current expected timeline is, but it should be this year.

That said, you can use the JDBC driver in DBeaver by configuring a generic jdbc driver connection. Our integration will just provide our driver pre-configured.

Best,

Thomas

Hi José!

This is an important feature and is planned to be implemented for a future release, not sure about the timing at this point!

Hi Kurro!

Thanks for your article and trying out IntegratedML. To hopefully point you in the right direction:

1. IntegratedML is not "just neural networks", but rather an autoML pipeline (see AutoML Guide) that first tests several ML methods on a subset of the data, then performs a training run using the full data using the ML method (neural networks, logistic regression, random forests, etc) that performed best on the subset of the data. In fact, by default, for regression problems like this, we only use XGBRegressor -- so in this case the method that IntegratedML uses is not a neural network at all!

2. "TRAIN MODEL" only needs to be called once per training dataset. Looping over the examples is handled inside that call.

3. This is potentially too small a dataset to produce reliable results. IntegratedML splits the data internally into training and testing subsets, so you would probably get better output if you have at least 100 random examples.

Kind Regards,

Thomas