## Powerful. Pragmatic. Explainable.

aivis works close to real-world problems, thus minimizing the effort from data import to result dramatically

### Industrial data-driven challenges are demanding

Thousands of parameters, highly dynamic environments, and the highest standards demand the maximum performance, flexibility, and usability from an machine learning solution. Most conventional methods are just not up to the task: They are either too slow, too expensive, too complicated to use, or just don’t deliver the required quality and therefore limit the industrial success.

### aivis is different

Developed for demanding industrial requirements, aivis delivers great results incredibly fast, can cope with almost any data complexity, and offers explainability. It helps industrial applications to get the most out of their data. Compared to other AutoML solutions, aivis is faster, provides better results, and requires less training data. It easily handles thousands of parameters and offers full explainability.

## Benefits

aivis opens up a whole new access to your data. Being close to industrial applications, it enables you to get the most out of your data fast and efficient. No excessive data preparation and no data science expertise needed.

## Features

aivis combines the best of different worlds being fast, flexible, stable, and transparent at the same time.

## Game-changing math

aivis is an industry-oriented and industry-agnostic machine learning technology that builds on the entirely new mathematical theory of Geometrical Kernel Machines: Kernel machines extended by methods from Stochastic Differential Geometry and Quantum Field Theory.

#### Data segmentation

The first step aivis always performs on the data is a data segmentation: Stochastic Differential Geometry is used to find a representation of the data that reveals all dependencies and relationships. Based on that, a proprietary universal cluster algorithm clusters similar geometries. The result is a segmentation of the data where each segment pools similar relationships and importances of the features.

#### Defining the metric

Next, for each segment the metric is determined that best describes its dependencies. In a way, this can be interpreted as calculating the hyperparameters, which eliminates the need for choosing and tuning hyperparameters.

#### Model building

Also, aivis builds and trains a model for each segment. This is accomplished by using methods from Quantum Field Theory: The labels are interpreted as vacuum states of a scalar quantum field whereas the hyperparameters are interpreted as vacuum expectation values of background fields (e.g. Higgs field). Finally, the models are combined to a strong ensemble predictor.

In summary, the preceding segmentation brings two vital benefits: First of all, it is relatively easy to train a model on the segmented data since aivis initially revealed all significant importances and relationships. Second, the results are transparent and interpretable since it is clear which segment contributes to the current prediction to which amount.

## Comparison

Due to it’s unique nature and mathematical approach, aivis provides great features and benefits when compared to other AutoML solutions and providers.