Particle physics is a source of engineering challenges, also for Machine Learning techniques. We showcase three current uses of Machine Learning in the LHCb experiment, one of the four main experiments of the Large Hadron Collider (LHC) at CERN. Two are in the Real Time Analysis framework, which is in charge of processing the detector 4TB/s dataflow in real time: one to locate the points where particles issued from the accelerator collisions decay, and the other to ensure a smooth choice in the data to be stored. A third use is about speeding the detector simulation with generative techniques. In all three cases, computing speed is the key factor for using Machine Learning algorithms.