Renewed modeling functions and added deployment functionsNEW
While the previous AutoML features did not allow for fine-grained tuning of the model, the newly added modeling now has a new concept of template.
The template can be configured for single model, ensemble learning, autopilot learning, etc., and hyperparameter settings can be configured at the same level as in Python code.
In the past, VARISTA always performed a parameter search when creating a model in AutoML, and it took a long time to complete the learning process.
With the new modeling feature, the modeling process has been reviewed and restructured so that it can now be used to quickly complete training on a single model, such as XGBoost.
In addition, ensemble learning and autopilot learning will compare up to 32 models and select the fused and superior model, which is ideal if you want to build a pipeline.
For more details, please check this template.
Model Evaluation Report
The UI has been revamped with a new layout, and the following charts can now be viewed in the regression.
- Prediction And Observation
- Residual VS Fitted
- Absolute Errors
- Errors Chart
- Residual Histogram
In Classification, you can now check Threshold, PrecisionRecall, ROC, and Confusion Matrix.
Data Distribution, Correlation as well as Partial Dependency Plot can now be checked.
In the past, VARISTA allowed you to run inference only in the browser, but with the new deployment feature, you can now deploy your model with a single click and use inference via the API.
You can also easily turn on/off the deployed API from the GUI.
For details, please see Deploy here.
A dashboard has been added to make it easier to keep track of the project status.
- Form Inference
It is now possible to perform inference using forms from the browser.
The form will automatically display the features, so you can quickly make inferences even if you don't want to create test data in CSV.