Abstract: Data Analytics Engines can be provided as a “black-boxed” abstraction to their “users.” This allows a user to mix and match analytical components if their input data matches the input requirement of the engine. Furthermore, by decoupling the Data Analytics Engine creation from the environment, a high degree of process automation, scalability, and improved maintainability can be achieved. As a result, Data Analytic engineers and Data Scientists can create reusable components for other scientists and business users, whereas the users need not know how the Engines are coded or the environment in which their engines will run, but only need to know the input schema of the data.

Abstract: Hyperparameter tuning for a machine learning model is performed in a massively parallel database system. A computer system comprised of a plurality of compute units executes a relational database management system (RDBMS), wherein the RDBMS manages a relational database comprised of one or more tables storing data. One or more of the compute units perform the hyperparameter tuning for the machine learning model, wherein the hyperparameters are control parameters used in construction of the model, and the tuning of the hyperparameters is implemented as an operation in the RDBMS that accepts training and scoring data for the model, constructs the model using the hyperparameters and the training data, and generates goodness metrics for the model using the scoring data.