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With Qlik AutoML, we make machine learning available to more than just professional data scientists. Your analytics users and teams can now become citizen data scientists and generate predictions in a simple and effective way for the “other 90% of use cases” not covered by data scientists – areas such as sales forecasting, churn reduction, customer acquisition, inventory optimization, spend analysis, etc. We offer a simple yet powerful, code-free ML workflow with AI-assistance and unlimited experimentation. Predictions are delivered with full explainability so you can see not just what might happen but why. And you can easily publish predictions or integrate models into Qlik Sense for real-time associative exploration and what-if scenario planning.
We’ve recently made a number of improvements to Qlik AutoML that will unlock even more value for customers and prospects looking to democratize the power of predictive AI. With these improvements you get more advanced feature engineering and explainability, better control over users and roles, more iterative experimentation, and improved performance. Specifically:
- We’ve added automated free text feature engineering, allowing far better prediction from free text fields. Under the hood we’re using advanced techniques to look for signal within free text strings, generating 10’s of thousands of possible features which are automatically evaluated for importance and used for prediction. All this is invisible to the user, but you’ll notice text fields will have much better predictive influence. 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. You can now re-use the same experiments, with the same version history and settings, when changing features in the data set or loading new sets of records. This means you can more easily test new features in an iterative manner, and refresh models with new historical data if business conditions have changed.
- 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.
Going forward, we are continuing to invest heavily in our AI roadmap, including our augmented analytics capabilities, predictive and generative AI, and even unstructured data insights. Stay tuned for much more to come in all these areas at our annual customer event – Qlik Connect, in June. REGISTER Today!