Reliable and performant heavy flavour identification is of prime importance for the physics
program of the CMS experiment. During the last years the CMS collaboration has dedicated a
considerable effort to improve and expand its capabilities in this sector by applying several
machine learning techniques well established in industry, but still experimental in HEP. The
poster will focus on a selection of these techniques and describe the implementation details as
well as the resulting gains.