A computer-implemented method of early prediction of base calling quality at at least one target cycle, during an extended optical base calling process that includes pre-prediction base calling process cycles, before a prediction point, and at least two times as many post-prediction base calling process cycles as there are pre-prediction cycles, after the prediction point and through the target cycle, wherein each base calling process cycle includes (a) chemical processing to attach an additional complementary nucleotide to a target nucleotide strand at millions of locations on a substrate, (b) camera positioning and image registration over image tiles of the substrate, and (c) image acquisition over the image tiles, the method including: invoking a trained convolutional neural network wherein the trained convolutional neural network was trained using base calling quality data comprising a plurality of training time series for the pre-prediction base calling process cycles and for one or more post-prediction target cycles, used as ground truth for training labels, the plurality of time series comprising time series data for chemical processing subsystem performance, such as phasing and pre-phasing errors, for image registration subsystem performance, such as x and y image offset adjustment after image capture, for image acquisition subsystem performance series, such as reports of focus and contrast, and for a cycle-by-cycle base calling quality estimate, such as a Phred score, inputting to the trained convolutional neural network a plurality of production time series from the pre-prediction base calling process cycles; the trained convolutional neural network determining at the prediction point after cycle n, from the pre-prediction base calling process cycles, a base calling quality expected at the target cycle, after at least two times as many post-prediction base calling process cycles as there are pre-prediction cycles; and outputting for operator evaluation at least the base calling quality expected at the target cycle, which is at cycle 3n or later. invoking a trained convolutional neural network wherein the trained convolutional neural network was trained using base calling quality data comprising a plurality of training time series for the pre-prediction base calling process cycles and for one or more post-prediction target cycles, used as ground truth for training labels, the plurality of time series comprising time series data for chemical processing subsystem performance, such as phasing and pre-phasing errors, for image registration subsystem performance, such as x and y image offset adjustment after image capture, for image acquisition subsystem performance series, such as reports of focus and contrast, and for a cycle-by-cycle base calling quality estimate, such as a Phred score, for chemical processing subsystem performance, such as phasing and pre-phasing errors, for image registration subsystem performance, such as x and y image offset adjustment after image capture, for image acquisition subsystem performance series, such as reports of focus and contrast, and for a cycle-by-cycle base calling quality estimate, such as a Phred score, inputting to the trained convolutional neural network a plurality of production time series from the pre-prediction base calling process cycles; the trained convolutional neural network determining at the prediction point after cycle n, from the pre-prediction base calling process cycles, a base calling quality expected at the target cycle, after at least two times as many post-prediction base calling process cycles as there are pre-prediction cycles; and outputting for operator evaluation at least the base calling quality expected at the target cycle, which is at cycle 3n or later.