The use of machine learning is increasing at the LHC experiments including both the ATLAS and LHCb collaborations, in terms of the number of users, the breadth of applications, and the set of different techniques under study. While traditionally applied in the context of improving the final analysis selection for a given physics result, machine learning is now also being applied in many other places, including object reconstruction, object calibration, object identification, simulation, and automation. The variety of machine learning tools being used is also expanding, and many areas are benefiting from the use of deep learning methods. It is expected that this growth in machine learning within particle physics will continue, as the large and rapidly increasing datasets provide the perfect environment to develop and refine new machine learning algorithms which can maximally exploit the complex data.