aivis can easily cope with thousands of parameters. This makes it especially useful in very complex environments where human comprehension quickly reaches its limits.

Before starting with resource-intensive operations, aivis first rotates the data in the right direction by data segmentation. This makes all following operations much easier and faster.

A data segmentation based on stochastic differential geometry creates data segments with similar properties and known relevant influences. This allows aivis only to learn relevant behavior and natively prevents overfitting.

The theory behind aivis allows a geometrical interpretation of hyperparameters enabling their direct determination. Therefore, aivis eliminates the need for choosing and tuning hyperparameters.

aivis builds trained models without any manual intervention. In particular, it requires no selection of mathematical approaches or filtering of irrelevant data.

The preceding data segmentation reveals the contribution of each signal or parameter, allowing excellent transparency and explainability of each result.

The initial clustering of the data into segments with similar behavior greatly simplifies the model building, enabling excellent quality.

Since aivis models natively include the system’s different types of behavior into the training, they are accurate over a long time without any adaptions.

aivis works close to real-world problems, thus minimizing the struggle to translate your question back and forth into something that mathematical approaches can solve.

The preceding data segmentation allows for independent model building for each segment. This is a very data-efficient approach that only requires little data to develop and train a complete model.

For a medium sized problem, aivis trains a model within a few minutes, where other approaches require at least a few hours and more. Even for complex approaches, aivis rarely needs more than one hour.

Neural networks in particular need very much training data in order to achieve an acceptable result. For aivis, a small amount of data is already sufficient.

Conventional models tend to lose their accuracy on slight changes in the system’s behavior, requiring constant adaptions. aivis models have already learned the underlying behavioral changes resulting in better accuracy over a much longer time.

aivis requires no data science expertise. All steps usually required in the machine learning process (feature engineering, algorithm selection and hyperparameter tuning, etc.) are taken care of.

Only to know what is happening is not enough. Understanding why it is happening is of equal importance. This fundamental realization forms the basis for every interaction with aivis making each result transparent and explainable.

A large part of data analytics is usually the translation of the problem into something that can be solved by a theoretical Machine Learning approach – and the retranslation. This effort can be avoided entirely when using aivis.

Conventional approaches can handle up to a few hundred parameters and require a large effort to do so. aivis can easily handle thousands of parameters without any additional effort.

Many approaches, especially neural networks, tend to overfit since they struggle to distinguish between noise and data. aivis uses data segmentation to avoid this problem from the very beginning.

aivis can cope with many of the commonly known data collection problems. In particular, it accepts data voids and non-equidistant timestamps on time-series data without problems.

Due to its data efficiency and high speed, aivis is particularly well suited for huge datasets as they can be expected in industrial environments.