The DNNLikelihood framework is presented and its main features discussed. Such framework encodes the experimental likelihood functions in deep neural networks and allows for a lightweight and platform-independent distribution of physics results through the ONNX model format. The procedure retains the full experimental information and does not rely neither on Gaussian approximation nor on dimensionality reduction and is applicable to both binned and un-binned likelihood functions. The distributed DNNLikelihood could be adopted for various use cases, such as re-sampling through Markov Chain MC techniques, combinations with other likelihood functions with proper treatments of correlations and re-interpretation with different statistical approaches. The presentation will be followed by a hands-on tutorial for illustrating the whole procedure with a pseudo-experiment corresponding to a realist LHC search for new physics.