  Particle identification (PID) is one of the main strengths of the ALICE
experiment at the LHC. It is a crucial ingredient for detailed studies of the
strongly interacting matter formed in ultrarelativistic heavy-ion collisions.
ALICE provides PID information via various experimental techniques, allowing
for the identification of particles over a broad momentum range (from around
100 MeV/$c$ to around 50 GeV/$c$). The main challenge is how to combine the
information from various detectors effectively. Therefore, PID represents a
model classification problem, which can be addressed using Machine Learning
(ML) solutions. Moreover, the complexity of the detector and richness of the
detection techniques make PID an interesting area of research also for the
computer science community. In this work, we show the current status of the ML
approach to PID in ALICE. We discuss the preliminary work with the Random
Forest approach for the LHC Run 2 and a more advanced solution based on Domain
Adaptation Neural Networks, including a proposal for its future implementation
within the ALICE computing software for the upcoming LHC Run 3.
