Automated free text feature engineering uses sophisticated algorithms under the hood to allows far better prediction from free text fields. This complements the date feature engineering capability we released last year, which automatically parses dates into usable features.

Organizations now have role-based access control for AutoML users. We’ve added two new user roles to support AutoML – experiment contributors and deployment contributors, which can be assigned to specific users or groups. With this, you can now control and limit access to AutoML to the right types of users.

You now get SHAP (explainability) data for multiclass problems. Prior to this, SHAP data was not available for these types of models, but now you can generate and download explainability data in the same manner as binary classification and regression-based models.

You can now change the data set for existing experiments. Users can now more easily iterate and refine experiments without having to create a new experiment when changing features or retraining models with new data sets.

We’ve improved the performance of the real-time connector. Real-time integration of predictive models within analytics apps supports associative exploration and what-if analysis. With this update, we’ve revised the architecture to improve speed and better support real-time production workloads.

https://community.qlik.com/t5/Product-Innovation/Predictive-AI-Continued-Innovation-in-AutoML/ba-p/2433849

Getting Started AutoML:
https://community.qlik.com/t5/Official-Support-Articles/How-To-Get-Started-with-Qlik-AutoML/ta-p/1983296

AutoML Tips and Things You Should Know:
https://www.youtube.com/playlist