All pieces of a puzzle are now in place for a productive and successful large scale risk modeling using Machine Learning. A slew of recent software and hardware announcements means that we finally have a full, brand new stack of components to have a shot at productive enterprise class Machine Learning risk modeling exercise. Google's TensorFlow makes it possible to quickly train, test and run predictive models on a variety of target devices ( CPU, GPU ). Latest TensorFlow releases incorporate tf.learn library that makes it much easier to extract features and pass datasets on to train, test and predict modules. This trend towards ease of use will continue with announced Keras incorporation into TensorFlow build. On a hardware side, IBM just released Power AI platform/appliance that, aside from TensorFlow, also incorporates Nvidia hardware and software ( GPUs, Cuda, NVLink ).