ABOUT craftworks AI
At craftworks AI, we build prize-winning Artificial Intelligence solutions for the Industry. We use state-of-the-art Computer Vision, Machine Learning and Deep Learning techniques to solve hard industrial problems using data. We continuously push the boundaries of what is possible through cutting-edge research. For this purpose, we partner with world-leading academic institutions and companies.
Meta Learning is a subfield of machine learning and is often also referred to as “learning to learn”. In Meta Learning, machine learning algorithms are trained to make predictions based on metadata, such as network architecture and data set characteristics, instead of raw data. Therefore, Meta Learning allows for generalization of learning problems and may lead to drastically increased efficiency when substituting otherwise expensive experimentation procedures. At craftworks, we research and use Meta Learning techniques for exactly that.
Applying machine learning to real world problems is not only time consuming and resource intensive, but also challenging. Automated Machine Learning (AutoML) takes care of large parts of the machine learning process, such as feature engineering, model selection and hyperparameter tuning in an autonomous fashion. This frees up time of data scientists and, additionally, enables people not familiar with machine learning to leverage its power. Therefore, a team of our engineers is working on developing cutting-edge AutoML software.
The field of Representation Learning is engaged with the automated discovery of optimum representations of data given a specific learning problem. Learning the representation instead of spending hours or even days crafting it manually using traditional feature engineering techniques is time saving, often leads to significantly superior outcomes and frequently exposes previously unknown structures in the data. At craftworks, we use Representation Learning techniques to solve challenges ranging from Natural Language Processing to Computer Vision.
Reinforcement Learning is an iterative optimization approach that is motivated by the way humans learn: trial-and-error. In combination with deep learning, Reinforcement Learning is used to teach machines highly complex games, make robots learn arbitrary tasks, optimize control systems of power plants and many more. At craftworks, we recognize the wide area of application and the huge potential of Reinforcement Learning. Therefore, we develop novel approaches for solving multi-agent problems with incomplete information.
Die Struktur der transportierten Güter verändert sich. Gleichzeitig wirft das Physical Internet immer deutlichere Schatten – für die Güterbahnen sind das keine guten Nachrichten. Mit dem „Backbone“-Projekt wollen craftworks GmbH, WU Wien, Fraunhofer Austria und die Rail Cargo Group die Bahn fit für das Physical Internet machen.read more
Crate.io partnered with craftworks to build a proof-of-concept for Illwerke VKW that uses CrateDB and machine learning to process, analyze, and deliver actionable business intelligence from the massive amounts of data being collected.read more
Nowadays, everybody is talking about Artificial Intelligence (AI). It has become a major buzzword of the yet young 21st century. But are we ready for a largely automated world driven by Artificial Intelligence?read more
Continuous Integration and Deployment for Machine Learning Applications
Simon Stiebellehner, Head of AI and Bernhard Redl, Data Engineer, craftworks GmbH
Deep Learning for Predictive Quality & Predictive Maintenance
Daniel Ressi, Data Scientist, craftworks GmbH
When Labelled Data is Scarce - Semi-Supervised Learning for Visual Inspection at Miba
Simon Stiebellehner, Head of AI, craftworks GmbH
We hope you all enjoy the summer so far. As we never stop growing, we are happy to introduce you to our new Project Manager Christoph. He is part...read more
craftworks was selected as the startup of the month for FACTORY’s June edition. Find out more about how our three founders Jakob, Simon, and Michael started the company, our...read more