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Solar flare prediction is a central problem in space weather forecasting andhas captivated the attention of a wide spectrum of researchers due to recentadvances in both remote sensing as well as machine learning and deep learningapproaches. The experimental findings based on both machine and deep learningmodels reveal significant performance improvements for task specific datasets.Along with building models, the practice of deploying such models to productionenvironments under operational settings is a more complex and oftentime-consuming process which is often not addressed directly in researchsettings. We present a set of new heuristic approaches to train and deploy anoperational solar flare prediction system for $\geq$M1.0-class flares with twoprediction modes: full-disk and active region-based. In full-disk mode,predictions are performed on full-disk line-of-sight magnetograms using deeplearning models whereas in active region-based models, predictions are issuedfor each active region individually using multivariate time series datainstances. The outputs from individual active region forecasts and full-diskpredictors are combined to a final full-disk prediction result with ameta-model. We utilized an equal weighted average ensemble of two baselearners' flare probabilities as our baseline meta learner and improved thecapabilities of our two base learners by training a logistic regression model.The major findings of this study are: (i) We successfully coupled twoheterogeneous flare prediction models trained with different datasets and modelarchitecture to predict a full-disk flare probability for next 24 hours, (ii)Our proposed ensembling model, i.e., logistic regression, improves on thepredictive performance of two base learners and the baseline meta learnermeasured in terms of two widely used metrics True Skill Statistic (TSS) andHeidke Skill core (HSS), and (iii) Our result analysis suggests that thelogistic regression-based ensemble (Meta-FP) improves on the full-disk model(base learner) by $\sim9\%$ in terms TSS and $\sim10\%$ in terms of HSS.Similarly, it improves on the AR-based model (base learner) by $\sim17\%$ and$\sim20\%$ in terms of TSS and HSS respectively. Finally, when compared to thebaseline meta model, it improves on TSS by $\sim10\%$ and HSS by $\sim15\%$.

Towards Coupling Full-disk and Active Region-based Flare Prediction for Operational Space Weather Forecasting