Adaptive immune receptor repertoires (AIRRs) capture past and present immune responses and therefore represent a powerful resource for developing diagnostics and therapeutics. Machine learning (ML) has the ability to discover complex sequence patterns and help further these diagnostic and therapeutic aims. However, to exploit these opportunities, it is necessary to overcome the intrinsic challenges of AIRR data: unknown rules determining antigen binding, high diversity and specificity of receptors with low overlap between AIRRs, and low signal-to-noise ratio. Further, different ML approaches need to be validated and compared before they could be deployed in practice. In this webinar, we will focus on standardized and reproducible ML workflows, benchmarking, and comparison of AIRR ML approaches. We will argue for the use of simulation for validation and benchmarking of ML methods before moving to experimental datasets.