51 citations · 117 across the 20 of their papers we have counts for
10 papers · 1 filter
Machine Learning Operations (MLOps): Overview, Definition, and Architecture
Dominik Kreuzberger, Niklas Kühl, Sebastian Hirschl
The final goal of all industrial machine learning (ML) projects is to develop ML products and rapidly bring them into production. However, it is highly challenging to automate and…
Utilizing Active Machine Learning for Quality Assurance: A Case Study of Virtual Car Renderings in the Automotive Industry
Patrick Hemmer, Niklas Kühl, Jakob Schöffer
Computer-generated imagery of car models has become an indispensable part of car manufacturers' advertisement concepts. They are for instance used in car configurators to offer cus…
What to Prioritize? Natural Language Processing for the Development of a Modern Bug Tracking Solution in Hardware Development
Thi Thu Hang Do, Markus Dobler, Niklas Kühl
Managing large numbers of incoming bug reports and finding the most critical issues in hardware development is time consuming, but crucial in order to reduce development costs. In…
Switching Scheme: A Novel Approach for Handling Incremental Concept Drift in Real-World Data Sets
Lucas Baier, Vincent Kellner, Niklas Kühl +1
Machine learning models nowadays play a crucial role for many applications in business and industry. However, models only start adding value as soon as they are deployed into produ…
DEAL: Deep Evidential Active Learning for Image Classification
Patrick Hemmer, Niklas Kühl, Jakob Schöffer
Convolutional Neural Networks (CNNs) have proven to be state-of-the-art models for supervised computer vision tasks, such as image classification. However, large labeled data sets…
How to Learn from Others: Transfer Machine Learning with Additive Regression Models to Improve Sales Forecasting
Robin Hirt, Niklas Kühl, Yusuf Peker +1
In a variety of business situations, the introduction or improvement of machine learning approaches is impaired as these cannot draw on existing analytical models. However, in many…
A network-based transfer learning approach to improve sales forecasting of new products
Tristan Karb, Niklas Kühl, Robin Hirt +1
Data-driven methods -- such as machine learning and time series forecasting -- are widely used for sales forecasting in the food retail domain. However, for newly introduced produc…
Handling Concept Drifts in Regression Problems -- the Error Intersection Approach
Lucas Baier, Marcel Hofmann, Niklas Kühl +2
Machine learning models are omnipresent for predictions on big data. One challenge of deployed models is the change of the data over time, a phenomenon called concept drift. If not…
Sequential Transfer Machine Learning in Networks: Measuring the Impact of Data and Neural Net Similarity on Transferability
Robin Hirt, Akash Srivastava, Carlos Berg +1
In networks of independent entities that face similar predictive tasks, transfer machine learning enables to re-use and improve neural nets using distributed data sets without the…
Machine Learning in Artificial Intelligence: Towards a Common Understanding
Niklas Kühl, Marc Goutier, Robin Hirt +1
The application of "machine learning" and "artificial intelligence" has become popular within the last decade. Both terms are frequently used in science and media, sometimes interc…