The first generation of Automated Machine Learning tools (from DataRobot, H20, Auger.AI and others) enables data scientists and business analysts to train with thousands of algorithm and hyperparameter combinations to generate the best possible predictive models. After uploading the data as a spreadsheet and waiting for training, the user selects the best model from the leaderboard and they are ready to do predictions.

Recently, several new AutoML products (from Google Cloud AutoML Tables, Microsoft Azure AutoML and Auger.AI’s open source A2ML API) have introduced the ability to automate the full AutoML process. Their APIs each support several phases in a pipeline: Importing Data, Training Against Algorithms and Hyperparameters, Evaluating Models, Deploying Models, Predicting Against New Data, and finally Reviewing the Performance of Models. These products all emphasize automated use by developers, not analysts uploading spreadsheets and viewing leaderboards.

Now that the full A2ML process can be automated new frontiers in exploiting AutoML’s capabilities are opened. Business logic in applications can be replaced by predictive models automatically generated from any data the developer has access to. Painstakingly coded sorting of results and lists of objects (accounts to manage, contacts to call, devices to maintain, trucks to route) can be ordered by a predictive model ranking. Complex cascades of if-then-else and switch statements (also known as business rules) derived from some “subject matter expert” or business person’s judgment can be replaced by the insights of a predictive model.

This use of AutoML has a far wider audience than just data scientists. Enterprise application developers can be far more productive and the amount of hard coded business logic in applications will steadily be reduced by use of predictive models. Software Has Eaten the World. With second generation AutoML, AI will now eat software.

This talk will describe in more detail just what second generation “Automated AutoML” entails. And describe several use cases where we have put this into effect for various applications and business problems.