Skip to main content


A model is a set of type and function definitions that constitutes meta-data descriptions about analyzed artifacts, along with mathematical/statistical AI models to infer data based observed data from the artifacts. For example, a model may infer that some device is shaking by using a mathematical/statistical model computation based on sensor reading from a device.

There is a number of predefined system models implementing some common machine learning algorithms, such as Linear regression, K-means, DBSCAN and Random forest.

User models can be defined that describe properties (meta-data) of devices such as machines and sensors along with model computations based on sensor readings from the device. The user models may be stand-alone mathematical/statistical models or inference models defined in terms of the predefined machine learning system models above.

The models are stored in model folders as text files containing OSQL scripts. The files can be edited through SA Studio or some text editor and loaded into SA Engine peers. The section Managing models explains how to manage models in SA Engine.


Model management functions