A cloud-powered machine learning platform, tailors models for precise predictions in various business scenarios with efficient data handling.
IQ.catalyst offers comprehensive capabilities for compiling, training, and validating machine learning models tailored to specific business scenarios. It leverages the scalability and flexibility of the cloud to seamlessly handle vast datasets, empowering users to predict key business figures with precision.
INFORMED DECISION-MAKING
The influence of complex correlations is more accurately represented in results
Streamlined model definition and training replace complex development cycles
Model maintenance uses historical data in leu of expensive human analytics to formulate results
Subscription-based cloud services reduce internal IT oversight through optimized data processes
IQ.CATALYST
Leveraging our innovative multi-tenant Business Technology Platform (BTP) service, IQ.catalyst, VCPowerPack now incorporates robust Machine Learning capabilities. With seamless integration into our Machine Routing Customizing (MRC), the need for manual maintenance of machine performance data becomes obsolete. Machine Learning models can be trained automatically through data extraction, providing a dynamic and efficient approach to AI within the product configuration.
Model Types refer to a specification of input features that exert influence on the prediction of a specified target feature. This influence is determined based on training data that aligns with the defined characteristics of the model.
Model Versions denote the administrative entity linked to a trained machine learning model, which undergoes testing and subsequent release to relevant systems.
IQ.catalyst streamlines the model definition and training processes, providing a more efficient way to develop machine learning models. This results in quicker deployment of models into production, enabling the company to respond rapidly to business needs and changing conditions.
IQ.catalyst utilizes historical data for model maintenance, allowing for adaptive learning and adjustments to changing trends. This is a cost-effective way to keep models up-to-date without the need for frequent human intervention and analysis.
In the realm of machine learning, the concept of slow down is obviated, and there is no requirement for setup times. Instead, the focus is on determining the final predicted or calculated machine speed.
Utilizing the innovative multi-tenant Business Technology Platform (BTP) service, IQ.catalyst, has integrated powerful Machine Learning capabilities. This service, running on BTP, is designed to serve various systems efficiently. The machine learning models essential for this capability are stored on an external object store.
