A processor-implemented method for making predictions relating to products manufactured via a manufacturing process, the method comprising: receiving input data; making a first prediction based on the input data; identifying a first machine learning model from a plurality of machine learning models based on the first prediction; making a second prediction based on the input data and the first machine learning model, wherein the second prediction is a prediction of a defect level associated with the products; transmitting a signal to adjust the manufacturing of the products based on the second prediction; wherein the method further comprises: generating the plurality of machine learning models including: applying a first baseline machine learning model to a training dataset; engaging in an error analysis process in response to applying the first baseline machine learning model, wherein the error analysis process comprises identifying a first portion of the training dataset that is within a threshold range of error, and identifying a second portion of the training dataset that is outside of the threshold range of error; labeling the first portion of the training dataset with a first label; and engaging in the error analysis process with a new baseline machine learning model for the second portion of the training dataset, wherein the first prediction includes a predicted label, the method further comprising: training a classifier based on the training dataset and labels generated by the error analysis process, wherein making the first prediction includes applying the classifier to the input data. receiving input data; making a first prediction based on the input data; identifying a first machine learning model from a plurality of machine learning models based on the first prediction; making a second prediction based on the input data and the first machine learning model, wherein the second prediction is a prediction of a defect level associated with the products; transmitting a signal to adjust the manufacturing of the products based on the second prediction; wherein the method further comprises: generating the plurality of machine learning models including: applying a first baseline machine learning model to a training dataset; engaging in an error analysis process in response to applying the first baseline machine learning model, wherein the error analysis process comprises identifying a first portion of the training dataset that is within a threshold range of error, and identifying a second portion of the training dataset that is outside of the threshold range of error; labeling the first portion of the training dataset with a first label; and engaging in the error analysis process with a new baseline machine learning model for the second portion of the training dataset, wherein the first prediction includes a predicted label, the method further comprising: training a classifier based on the training dataset and labels generated by the error analysis process, wherein making the first prediction includes applying the classifier to the input data. generating the plurality of machine learning models including: applying a first baseline machine learning model to a training dataset; engaging in an error analysis process in response to applying the first baseline machine learning model, wherein the error analysis process comprises identifying a first portion of the training dataset that is within a threshold range of error, and identifying a second portion of the training dataset that is outside of the threshold range of error; labeling the first portion of the training dataset with a first label; and engaging in the error analysis process with a new baseline machine learning model for the second portion of the training dataset, applying a first baseline machine learning model to a training dataset; engaging in an error analysis process in response to applying the first baseline machine learning model, wherein the error analysis process comprises identifying a first portion of the training dataset that is within a threshold range of error, and identifying a second portion of the training dataset that is outside of the threshold range of error; labeling the first portion of the training dataset with a first label; and engaging in the error analysis process with a new baseline machine learning model for the second portion of the training dataset, wherein the first prediction includes a predicted label, the method further comprising: training a classifier based on the training dataset and labels generated by the error analysis process, wherein making the first prediction includes applying the classifier to the input data.