A system for monitoring a recoat in an additive manufacturing machine, the system comprising: an automatic defect recognition subsystem including: a model catalog of a plurality of predictive models, each of the plurality of predictive models applicable to one of a plurality of products and to one of a plurality of recoat error indications; each of the plurality of predictive models representative of the recoat error indications at a pixel level of an image captured during recoat operations on the additive manufacturing machine; and an online monitoring subsystem including: an image classifier unit configured to classify the recoat error indications at the pixel level based on one of a selection of the plurality of predictive models; and a virtual depiction unit configured to create a virtual depiction of an ongoing build from successive captured images; and a processor unit configured to execute executable instructions that cause the processor unit to perform a method of monitoring an ongoing additive manufacturing build for the recoat error indications, classify a detected indication of the recoat error indications into a defect classification, and provide a determination regarding a severity of the detected indication to the ongoing additive manufacturing build, the method including: building a set of initial recoat indication models in an offline learning unit configured to implement self-learning techniques to process synthesized images of the recoat error indications; optimizing the model set by supplementing the model set with manually annotated/labeled examples; and storing the model set as the plurality of predictive models in the model catalog, each of the plurality of predictive models representing a particular type of the recoating error indications as it might appear on a particular additive manufacturing machine domain when producing a particular part. an automatic defect recognition subsystem including: a model catalog of a plurality of predictive models, each of the plurality of predictive models applicable to one of a plurality of products and to one of a plurality of recoat error indications; each of the plurality of predictive models representative of the recoat error indications at a pixel level of an image captured during recoat operations on the additive manufacturing machine; and a model catalog of a plurality of predictive models, each of the plurality of predictive models applicable to one of a plurality of products and to one of a plurality of recoat error indications; each of the plurality of predictive models representative of the recoat error indications at a pixel level of an image captured during recoat operations on the additive manufacturing machine; and an online monitoring subsystem including: an image classifier unit configured to classify the recoat error indications at the pixel level based on one of a selection of the plurality of predictive models; and a virtual depiction unit configured to create a virtual depiction of an ongoing build from successive captured images; and an image classifier unit configured to classify the recoat error indications at the pixel level based on one of a selection of the plurality of predictive models; and a virtual depiction unit configured to create a virtual depiction of an ongoing build from successive captured images; and a processor unit configured to execute executable instructions that cause the processor unit to perform a method of monitoring an ongoing additive manufacturing build for the recoat error indications, classify a detected indication of the recoat error indications into a defect classification, and provide a determination regarding a severity of the detected indication to the ongoing additive manufacturing build, the method including: building a set of initial recoat indication models in an offline learning unit configured to implement self-learning techniques to process synthesized images of the recoat error indications; optimizing the model set by supplementing the model set with manually annotated/labeled examples; and storing the model set as the plurality of predictive models in the model catalog, each of the plurality of predictive models representing a particular type of the recoating error indications as it might appear on a particular additive manufacturing machine domain when producing a particular part. building a set of initial recoat indication models in an offline learning unit configured to implement self-learning techniques to process synthesized images of the recoat error indications; optimizing the model set by supplementing the model set with manually annotated/labeled examples; and storing the model set as the plurality of predictive models in the model catalog, each of the plurality of predictive models representing a particular type of the recoating error indications as it might appear on a particular additive manufacturing machine domain when producing a particular part.