Drift detection for machine learning operation

A method for predicting drift of a machine learning algorithm deployed in a printed circuit board manufacturing process, the method comprising:

• obtaining a first sequence and a second sequence of measurement data of at least one parameter associated with at least one component of an instance of the printed circuit board, wherein the first sequence of measurement data was used for training the machine learning algorithm and the second sequence of measurement data was not used for training the machine learning algorithm;
• splitting each of the first and second sequences of the measurement data into a respective subset of norm measurement data and a respective subset of outlier measurement data;
• determining a similarity measure between the subset of norm measurement data of the first sequence and the subset of norm measurement data of the second sequence and determining a further similarity measure between the subset of outlier measurement data of the first sequence and the subset of outlier measurement data of the second sequence;
• and
• determining a prediction of the drift of the machine learning algorithm based on the similarity measure and the further similarity measure;
• reducing a dimension of each of the subsets of norm measurement data of the first and the second sequence and the subsets of outlier measurement data of the first sequence and the second sequence, and
• when a drift of a machine learning algorithm is predicted or detected, using a drift adaptation technique to improve the performance of the machine learning algorithm on new data.