Methods, systems, and apparatuses for adapting a predictive model for a manufacturing process. One method includes receiving, with an electronic processor, a plurality of data points for a plurality of manufactured parts and the predictive model. The predictive model outputs a label for a manufactured part provided by the manufacturing process indicating whether the manufactured part is accepted or rejected. The method also includes estimating, with the electronic processor, a drift for each of the plurality of data points and generating, with the electronic processor, an adapted version of the predictive model based on the predictive model and the drift for each of the plurality of data points. In addition, the method includes outputting, with the electronic processor, a label for each of the plurality of manufactured parts using the adapted version of the predictive model.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of adapting a predictive model for a manufacturing process, the method comprising: receiving, with an electronic processor, the predictive model, the predictive model outputting a label for a manufactured part provided by the manufacturing process, wherein the label indicates whether the manufactured part is accepted or rejected; receiving, with the electronic processor, a plurality of data points associated with a plurality of manufactured parts; estimating, with the electronic processor, a drift for each of the plurality of data points; generating, with the electronic processor, an adapted version of the predictive model based on the predictive model and the drift for each of the plurality of data points; and outputting, with the electronic processor, a label for each of the plurality of manufactured parts using the adapted version of the predictive model. 2. The method of claim 1, further comprising: estimating, with the electronic processor, a plurality of labels associated with the plurality of manufactured parts based on the plurality of data points and the predictive model, the plurality of labels indicative of whether each of the plurality of manufactured parts are within specifications. 3. The method of claim 2, further comprising: generating, with the electronic processor, a temporary predictive model based on the plurality of data points and the plurality of labels. 4. The method of claim 3, further comprising: comparing, with the electronic processor, the adapted version of the predictive model and the temporary predictive model to obtain a difference; and when the difference is greater than a threshold, updating, with the electronic processor, the adapted version of the predictive model. 5. The method of claim 4, wherein comparing the adapted version of the predictive model and the temporary predictive model to obtain the difference includes calculating a Kullback-Leibler Divergence between the adapted version of the predictive model and the temporary predictive model. 6. The method of claim 4, wherein updating the adapted version of the predictive model includes (a) calculating a gradient of the difference at each of the plurality of data points; (b) updating the drift at each of the plurality of data points based on the gradient at each of the plurality of data points; (c) updating the adapted version of the predictive model based on the updated drift for each of the plurality of data points to obtain an updated adapted version of the predictive model; (d) updating the plurality of labels associated with the plurality of manufactured parts based on the plurality of data points and the updated adapted version of the predictive model; (e) generating a new temporary predictive model based on the plurality of data points and the updated plurality of labels; (f) comparing the updated adapted version of the predictive model and the new temporary predictive model to obtain an updated difference; (g) when the updated difference is greater than the threshold, repeating (a) through (f); and (h) when the updated difference is less than the threshold, applying the updated adapted version of the predictive model. 7. The method of claim 1, wherein generating the adapted version of the predictive model based on the predictive model and the drift for each of the plurality of data points includes (a) setting the adapted version of the predictive model to the predictive model; (b) generating a temporary predictive model based on the plurality of data points; (c) comparing the adapted version of the predictive model and the temporary predictive model to determine a difference; and (d) when the difference is greater than a threshold, updating the drift for each of the plurality of data points, updating the adapted version of the predictive model based on the updated drift for each of the plurality of data points, and repeating (b) through (d) using the updated adapted version of the predictive model as the adapted version of the predictive model. 8. A system for adapting a predictive model for a manufacturing process, the system comprising: a database storing a plurality of data points associated with a plurality of manufactured parts; and a controller communicably coupled to the database, the controller including an electronic processor, the electronic processor configured to receive the plurality of data points; receive the predictive model, the predictive model outputting a label for a manufactured part provided by the manufacturing process, wherein the label indicates whether the manufactured part is accepted or rejected; estimate a drift for each of the plurality of data points; generate an adapted version of the predictive model based on the predictive model and the drift for each of the plurality of data points; and output a label for each of the plurality of manufactured parts using the adapted version of the predictive model. 9. The system of claim 8, wherein the electronic processor is further configured to estimate a plurality of labels associated with the plurality of manufactured parts based on the plurality of data points and the predictive model, the plurality of labels indicative of whether each of the plurality of manufactured parts are within specifications. 10. The system of claim 9, wherein the electronic processor is further configured to generate a temporary predictive model based on the plurality of data points and the plurality of labels. 11. The system of claim 10, wherein the electronic processor is further configured to compare the adapted version of the predictive model and the temporary predictive model to obtain a difference; and when the difference is greater than a threshold, update the adapted version of the predictive model. 12. The system of claim 11, wherein the electronic processor is configured to compare the adapted version of the predictive model and the temporary predictive model to obtain the difference by calculating a Kullback-Leibler Divergence between the adapted version of the predictive model and the temporary predictive model. 13. The system of claim 11, wherein the electronic processor is further configured to (a) calculate a gradient of the difference at each of the plurality of data points; (b) update the drift at each of the plurality of data points based on the gradient at each of the plurality of data points; (c) update the adapted version of the predictive model based on the updated drift for each of the plurality of data points; (d) update the plurality of labels associated with the plurality of manufactured parts based on the plurality of data points and the updated adapted version of the predictive model; (e) generate a new temporary predictive model based on the updated plurality of data points and the updated plurality of labels: (f) compare the updated adapted version of the predictive model and the new temporary predictive model to obtain an updated difference; (g) when the updated difference is greater than the threshold, repeat (a) through (f); and (h) when the updated difference is less than the threshold, apply the adapted version of the predictive model. 14. The system of claim 8, wherein the electronic processor is further configured to (a) set the adapted version of the predictive model to the predictive model; (b) generate a temporary predictive model based on the plurality of data points; (c) compare the adapted version of the predictive model and the temporary predictive model to determine a difference; and (d) when the difference is greater than a threshold, update the drift for each of the plurality of data points, update the adapted version of the predictive model based on the updated drift for each of the plurality of data points, and repeat (h) through (d) using the updated adapted version of the predictive model as the adapted version of the predictive model. 15. A non-transitory computer-readable medium containing instructions that, when executed by an electronic processor, perform a set of functions comprising: receiving a predictive model, the predictive model outputting a label for a manufactured part provided by a manufacturing process, wherein the label indicates whether the manufactured part is accepted or rejected; receiving a plurality of data points associated with a plurality of manufactured parts; estimating a drift for each of the plurality of data points; generating an adapted version of the predictive model based on the predictive model and the drift for each of the plurality of data points; and outputting a label for each of the plurality of manufactured parts using the adapted version of the predictive model. 16. The non-transitory computer-readable medium of claim 15, wherein the set of functions further comprises: estimating a plurality of labels associated with the plurality of manufactured parts based on the plurality of data points and the predictive model, the plurality of labels indicative of whether each of the plurality of manufactured parts are within specifications, generating a temporary predictive model based on the plurality of data points and the plurality of labels; comparing the adapted version of the predictive model and the temporary predictive model to obtain a difference; and when the difference is greater than a threshold, updating the adapted version of the predictive model to obtain an updated adapted version of the predictive model. 17. The non-transitory computer-readable medium of claim 16, wherein comparing the adapted version of the predictive model and the temporary predictive model to obtain the difference includes calculating a Kullback-Leibler Divergence between the adapted version of the predictive model and the temporary predictive model. 18. The non-transitory computer-readable medium of claim 16, wherein generating the updated adapted version of the predictive model includes (a) calculating a gradient of the difference at each of the plurality of data points; (b) updating the drift at each of the plurality of data points based on the gradient at each of the plurality of data points; (c) updating the adapted version of the predictive model based on the updated drift for each of the plurality of data points; (d) updating the plurality of labels associated with the plurality of manufactured parts based on the plurality of data points and the updated adapted version of the predictive model, the updated plurality of labels indicative of whether each of the plurality of manufactured parts are within specifications; (e) updating the temporary predictive model based on the plurality of data points and the updated plurality of labels; (f) comparing the updated adapted version of the predictive model and the new temporary predictive model to obtain an updated difference; (g) when the updated difference is greater than the threshold, repeating (a) through (f); and (h) when the updated difference is less than the threshold, using the adapted version of the predictive model. 19. The non-transitory computer-readable medium of claim 15, wherein updating the adapted version of the predictive model based on the predictive model and the drift for each of the plurality of data points includes (a) setting the adapted version of the predictive model to the predictive model; (b) generating a temporary predictive model based on the plurality of data points; (c) comparing the adapted version of the predictive model and the temporary predictive model to determine a difference; (d) when the difference is greater than a threshold, updating the drift for each of the plurality of data points, updating the adapted version of the predictive model based on the updated drift for each of the plurality of data points, and repeating (b) through (d) using the updated adapted version of the predictive model as the adapted version of the predictive model.