DATA SCIENCE
‘Big data lays the foundations for companies to undergo digital transformation. If a business isn’t yet utilizing data lake technology, they should at least be looking at the option. In the modern world, these kinds of solutions are key to production. They enable businesses to store huge volumes of data at low cost and act as a fast and flexible data management platform.
Companies that want to access and effectively process a wide variety of data in real-time to enable them to come up with solutions for highly specialized and complex issues will find that data lakes offer the perfect architecture in which to do so – and that Unbelievable Machine is the perfect partner.’
Dr. Olivia Lewis
Teamlead in Applications
Artificial Intelligence
There are few companies that have access to a large enough team of data science experts to enable them to fully exploit the potential opportunities and applications of artificial intelligence. But there’s a way around this challenge: partner up. In addition to their in-depth knowledge of AI and machine learning, *um experts have the industry know-how to identify potential and guide projects through to successful completion.
Neural networks and deep learning applications not only demand the availability of enormous data sets – it is also important to ensure that the results generated by the black box of AI algorithms are accurate. This process is subject to a whole different set of rules than those that apply in more ‘traditional’ software development settings.
Machine Learning
Machine learning is a component of artificial intelligence. It enables IT systems to use automatically created models to detect patterns and regularities in datasets and to independently derive solutions to specific problems from this information. These findings can be generalized and then – after a short learning phase – be applied to new instances or used to analyze previously unknown data. To enable the machine to learn and propose solutions independently, it is trained by *um experts.
These experts create the architecture and algorithms, verify the quality of the data and access to the required data sources, and create rules for data analysis and pattern recognition. Once this process is complete, the learning machine can be used to make predictions based on the data it has analyzed, calculate the probability of specific events, optimize processes based on identified patterns, and independently adapt to developments.
Bots, assistance systems, and recommendations
AI-based applications also enable businesses to evaluate data on a scale that no human would ever be capable of processing. These applications – known as recommendation engines for cross-selling – are able to recognize complex patterns of customer behavior and derive recommendations from this data. Using natural language processing, we can create assistance systems (bots) that use natural language to replace instruction manuals or to execute voice commands. The various open-source libraries that are already available in the field of image and language recognition – such as Google Tensorflow and Microsoft Azure – offer a wide range of functions. However, in-depth data expertise is key to adapting these resources to your individual business needs.
Image processing for process automation
Image recognition and processing algorithms are increasingly automating tasks that previously required human input. At the highest level, these algorithms will enable cars to drive themselves; using sensors, lidar, radar, and image recognition technology, cars will ultimately be able to perceive their environment as effectively as a human driver. Even now, machine learning is being used to automate processes based on image data: Machines can already independently sort objects or use algorithms to detect faults in car body paintwork that previously would have had to be spotted in a human quality check.
Predictive Maintenance
Big data analytics helps businesses to optimize processes. Predictive maintenance has gained a firm foothold in industry as one of the very first use cases: Bringing sensor and process data together enables companies to move away from rigid maintenance cycles toward a more flexible model. Machines are not subjected to maintenance too soon, but are caught in time before a critical part fails and production grinds to a halt. If the machine is running hotter than normal, the noise level has changed, the machine is smoking, or the temperature has dropped, the sensor networks can detect these anomalies at an early stage to prevent a costly full-system failure. But there’s a long and complex journey between implementing initial pilot projects and rolling out predictive maintenance on a larger scale. *um experts help companies to make predictive maintenance in production and product processes part of their everyday operations.
Beyond production environments, anomaly detection is a useful tool in a wide range of other sectors and scenarios: From detecting fraud in the financial sector and identifying attack patterns in IT security, to spotting errors in any kind of process. With experience in a wide range of projects in the field of predictive maintenance and anomaly detection, *um possesses in-depth expertise in a variety of industries, including the automotive, commercial, and energy sectors. We create systems that will make an immediate impact on your business.
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