For tabular data, the modeling wizard currently suggests a model based on the input data. As part of a research project we have identified a better modeling scheme for this. The model proposed is a bit more complicated and requires some more computation time but should result in better model quality. In the picture below is an illustration of the changes made. To the left is the previous and to the right the new model the wizard created for a particular case.
Improved wizard models for tabular data
Peltarion Prediction Server
Bring your platform built models straight to production on your own infrastructure with the Peltarion Prediction Server. This feature allows you to download, build and host your model as a Docker container with just a few steps:
- Export your model including the container definition from the deployment page
- Execute the Docker build command
- Execute the Docker run command
You can then already run inferences on the mode by calling the API and similarly you can deploy this container to any registry and start using it. More info here.
/The Peltarion product team
It is now possible to archive and unarchive experiments. When archiving an experiment it will not be available for modeling, evaluation, inspection or deployment. It is however not deleted and by unarchiving it, it is available like before. The reason for archiving experiments is to get a less cluttered experiment list and thereby improve the workflow when you have many experiments.
Experiments can be archived by selecting the experiment you wish to archive, click on the Experiment options menu (the three dots on the top right corner) and select “Archive”. To restore the archived experiment, select “Unarchive”.
Active experiment adherance
From now on, you will be able to move seamlessly between evaluation and modelling view and stay within your selected experiment. In other words, if you are evaluating a model and then switch back to modelling view to do something there, the model you are currently working with is the one that will appear in this view.
Improved Web App results
Brand new result representation in our web app with better support for categorical and binary outputs including listing multiple results for multi-modal outputs.
Try it out yourself on the platform with most models. Just head to the deployment page, enable a deployment and open the web app.