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Applications of machine learning on NLP tasks today receive a lot of attention and have been shown to yield state of the art results on a wide range of tasks. We describe several cases where machine learning is deployed productively under the usual constaints of real-world projects: Real-world requirements, fast throughput, reasonably low requirements in terms of training corpus size and high quality results. What we observe is a general trend towards open source - also our components are open source. With the software being mostly freely available, among the key success criteria for many NLP projects today therefore is first and foremost the necessary expertise required to combine, tune and apply open source components.