09:00 - 10:00
In many real world scenarios running inference of a trained model in a third party cloud service is not desirable. Especially in an enterprise setting, the customer often wishes more control over the server infrastructure. The project requirements may include custom cloud or other traditional server infrastructure in which an ML solution is to be integrated. Java is still the most widespread platform for enterprise server systems. Adding a new language or framework in such projects is cumbersome and increases risk and cost. In this talk the possibilities to run inference of trained Tensorflow Models in a Java Enterprise Server environment are discussed, together with real world examples of integration into popular server frameworks like Spring and Apache CXF. Different possibilities for deployment and version control of trained models are explored.