As experts from a relatively young industry, we know about the potential of machine learning but see the untapped opportunities at the same time. Bridging this gap unlocks enormous value but also requires thoughtful change. Therefore, bridging this gap is not just a matter of technology or its implementation - it is also a matter of how change is approached. Introducing methods of NLP, as one of the most disruptive disciplines of this century, will make machines understand - but also people need to do so. Bringing all together - approach, technology and especially our clients - is the recipe for generating value. This is the rationale behind deepset and it is what drives our work everyday. The age of making machines talk can be one of the most exciting episodes in human history. We are here to write it with you!


Malte Pietsch gained experience as a Data Scientist in various industries. At deepset he is developing NLP models and is responsible for transforming raw algorithms into business applications.

He holds a M.Sc. with honors from TU Munich and University of Augsburg and conducted research in the area of dynamic risk prediction at Carnegie Mellon University. In previous projects he developed models to predict the outcome of legal claims at flightright and to improve the wording of online ads at plista. Malte regularly shares his thoughts and findings as a speaker at international machine learning conferences.
Milos Rusic gained experience in delivering AI and machine learning technologies to industrial clients. At deepset he is responsible for the client interaction, project delivery and the development of the business.

During his studies at UC Berkeley, TU Munich and University of Augsburg he trained himself in finance and information systems. Furthermore he holds an Honour's Degree from the Center for Digital Technology and Management (CDTM) in Munich. Milos has a proven track record in business development and the delivery of AI projects to industrial customers. His methodological skills were developed in various academic papers which were presented at prestigious conferences like the International Conference on Information Systems (ICIS).
Timo Möller has worked with neural networks for over a decade and has been following recent trends from convolutional neural networks on images to recurrent neural networks for generating text. At deepset he creates and maintains everything related to our NLP models.

He has studied AI and computational neuroscience at the University of Maastricht and TU Berlin. Since then he has gained several years of industry experience mainly in transforming and analyzing large scale text streams.


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