Machine learning and computing tools for LHC Run3 and HL-LHC: challenges and opportunities

 At the dawn of LHC Run 3, machine learning enjoys an enormous success on many fronts of the experimental high energy physics program: event reconstruction, interpretation, background estimation, and many more. 

At the same time, the ambitious high luminosity program of the LHC that will develop over the next two decades poses new computing and physics challenges to the HEP community. 

In this talk I will summarize some applications of machine learning developed to tackle current and future challenges, with a particular focus on innovative analysis techniques and data quality monitoring at the CMS experiment.