Machine Learning: Learning from data
Our consulting offers advice and implementation for machine learning projects
Machine Learning is concerned with giving computers the ability to learn on their own. A further development of pattern recognition and computer learning theory in the context of artificial intelligence, machine learning deals with complex algorithms that can learn from and make predictions about data.
We specialise in machine learning and develop intelligent systems that can find patterns often not recognisable by humans and contribute to decision-making in the company. In machine learning, we develop solutions after whose implementation, for example, patterns and objects in images and video recordings are recognised, identified and thus further information is obtained.
Avoiding mistakes through data-based decision-making processes
By applying machine learning, our clients' management can make computer-based decisions. In particular, clients in logistics, manufacturing, retail, security, agricultural monitoring, banking and industrial technologies benefit from our machine learning services. The goal of our consulting is always to automate the process workflow and minimise the error rate. With the help of self-learning algorithms, complex problems can be solved in such projects.
In the area of machine learning, we have focused on the following services
Automate the process workflow and minimise the error rate with machine learning
Complex problems can be solved with the help of self-learning algorithms
Machine learning includes various technologies
Applications of Machine Learning
- Quality management:
By means of image or video analysis, it is possible to analyse all objects. Defective or low-quality components are then sorted out. This raises the quality level to a maximum. - Monitoring of hazardous areas:
In production, segments can be monitored in real time through video analysis and intelligently controlled by safety mechanisms in the event of potentially dangerous situations. - Reading out serial numbers:
All freely available information should also be used by a company. For example, the serial numbers of various components can be read out and stored. This ensures that the parts can be tracked precisely, so that it is possible to determine when, where and how many faulty batches have been produced. - Detect anomalies:
Quality management can also be supplemented by anomaly detection. If, for example, there is still a residual level of high-security components, it can be assumed that something went wrong during production. The artificial intelligence detects this and can send real-time notifications to the responsible persons. - Recognition of components:
Component sequencing can be automated using computer vision to initiate subsequent sorting.
