A method of training a machine learning model for use with data representative of optical fibre sensing measurements, the method comprising: training a first machine learning model using a first training dataset, wherein the first training dataset comprises data representative of optical fibre sensing measurements, and wherein the first training dataset is unlabelled; and using a second training dataset, training a second machine learning model that incorporates at least part of the trained first machine learning model, wherein the second training dataset comprises data representative of optical fibre sensing measurements, the second training dataset being smaller than, or equal in size to, the first training dataset; wherein the second machine learning model is configured to generate an output based on received data that is representative of an optical fibre sensing measurement performed by an optical fibre sensing system arranged to monitor a system or environment, the output being indicative of one or more of a property or variable for the monitored system or environment, an event detection, an event prediction, and an anomaly detection; the method being characterised in that : the first machine learning model is trained with the first training dataset via a self-supervised learning process; and the self-supervised learning process comprises partially masking data in the first training dataset, and training the first machine learning model to reconstruct data in the first training dataset from the partially masked data. training a first machine learning model using a first training dataset, wherein the first training dataset comprises data representative of optical fibre sensing measurements, and wherein the first training dataset is unlabelled; and using a second training dataset, training a second machine learning model that incorporates at least part of the trained first machine learning model, wherein the second training dataset comprises data representative of optical fibre sensing measurements, the second training dataset being smaller than, or equal in size to, the first training dataset; wherein the second machine learning model is configured to generate an output based on received data that is representative of an optical fibre sensing measurement performed by an optical fibre sensing system arranged to monitor a system or environment, the output being indicative of one or more of a property or variable for the monitored system or environment, an event detection, an event prediction, and an anomaly detection; the method being characterised in that : the first machine learning model is trained with the first training dataset via a self-supervised learning process; and the self-supervised learning process comprises partially masking data in the first training dataset, and training the first machine learning model to reconstruct data in the first training dataset from the partially masked data. the first machine learning model is trained with the first training dataset via a self-supervised learning process; and the self-supervised learning process comprises partially masking data in the first training dataset, and training the first machine learning model to reconstruct data in the first training dataset from the partially masked data. <EOS>