  We present Net2Vec, a flexible high-performance platform that allows the
execution of deep learning algorithms in the communication network. Net2Vec is
able to capture data from the network at more than 60Gbps, transform it into
meaningful tuples and apply predictions over the tuples in real time. This
platform can be used for different purposes ranging from traffic classification
to network performance analysis.
  Finally, we showcase the use of Net2Vec by implementing and testing a
solution able to profile network users at line rate using traces coming from a
real network. We show that the use of deep learning for this case outperforms
the baseline method both in terms of accuracy and performance.
