An electronic device may determine whether a machine-learning model is operating within predefined limits. In particular, the electronic device may receive, from another electronic device, instructions for the machine-learning model, a reference input and a predetermined output of the machine-learning model for the reference input. Note that the instructions may include an architecture of the machine-learning model, weights associated with the machine-learning model and/or a set of pre-processing transformations for use when executing the machine-learning model on images. In response, the electronic device may configure the machine-learning model based on the instructions. Then, the electronic device may calculate an output of the machine-learning model for the reference input. Next, the electronic device may determine whether the machine-learning model is operating within predefined limits based on the output and the predetermined output.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. An electronic device, comprising: an interface circuit configured to communicate, via a network, with another electronic device; memory configured to store a program module; and a processor coupled to the interface circuit and the memory, the processor configured to execute the program module to perform operations comprising: receiving, from the other electronic device, instructions for a machine-learning model, a reference input and a predetermined output of the machine-learning model for the reference input, wherein the instructions including an architecture of the machine-learning model, weights associated with the machine-learning model and a set of pre-processing transformations for use when executing the machine-learning model on images; configuring the machine-learning model based on the instructions; calculating an output of the machine-learning model for the reference input; and determining whether the machine-learning model is operating within predefined limits based on the output and the predetermined output. 2. The electronic device of claim 1, wherein the machine-learning model includes a neural network. 3. The electronic device of claim 1, wherein the instructions for the machine-learning model are based on training performed in a different computing environment than that of the electronic device. 4. The electronic device of claim 1, wherein the operations further comprise: when the machine-learning model is operating within the predefined limits, analyzing the images using the machine-learning model. 5. The electronic device of claim 4, wherein the operations further comprise: prior to the analysis, mapping the images to a set of input images having associated formats that are compatible with the machine-learning model using one or more of the set of pre-processing transformations. 6. The electronic device of claim 5, wherein a given pre-processing transformation is associated with at least a subset of the images based on an image context of at least the subset of the images; and wherein the image context includes at least one of: a format of at least the subset of the images, and an image characteristic associated with at least the subset of the images. 7. The electronic device of claim 6, wherein the instructions specify the image context, so that the given pre-processing transformation is applied to the subset of the images prior to the analysis using the machine-learning model. 8. The electronic device of claim 6, wherein the operations further comprise: determining the image context after receiving the instructions, so that the given pre-processing transformation is applied to the subset of the images prior to the analysis using the machine-learning model. 9. The electronic device of claim 8, wherein the determination of the image context is performed dynamically to improve a performance metric of the machine-learning model. 10. The electronic device of claim 1, wherein the operations further comprise: when the machine-learning model is operating outside of the predefined limits, providing a notification to the other electronic device and discontinuing analysis of the images using the machine-learning model. 11. The electronic device of claim 1, wherein the reference input includes an image pre-processed using one of the set of pre-processing transformations. 12. A non-transitory computer-readable storage medium storing instructions that, when executed by an electronic device, cause the electronic device to determine whether a machine-learning model is operating within predefined limits, by carrying out one or more operations that comprise: receiving, from another electronic device, instructions for the machine-learning model, a reference input and a predetermined output of the machine-learning model for the reference input, wherein the instructions including an architecture of the machine-learning model, weights associated with the machine-learning model and a set of pre-processing transformations for use when executing the machine-learning model on images; configuring the machine-learning model based on the instructions; calculating an output of the machine-learning model for the reference input; and determining whether the machine-learning model is operating within the predefined limits based on the output and the predetermined output. 13. The computer-readable storage medium of claim 12, wherein the machine-learning model includes a neural network. 14. The computer-readable storage medium of claim 12, wherein the instructions for the machine-learning model are based on training performed in a different computing environment than that of the electronic device. 15. The computer-readable storage medium of claim 12, wherein the operations further comprise: when the machine-learning model is operating within the predefined limits, analyzing the images using the machine-learning model. 16. The computer-readable storage medium of claim 15, wherein the operations further comprise: prior to the analysis, mapping the images to a set of input images having associated formats that are compatible with the machine-learning model using one or more of the set of pre-processing transformations. 17. The computer-readable storage medium of claim 16, wherein a given pre-processing transformation is associated with at least a subset of the images based on an image context of at least the subset of the images; and wherein the image context includes at least one of: a format of at least the subset of the images, and an image characteristic associated with at least the subset of the images. 18. The computer-readable storage medium of claim 17, wherein the instructions specify the image context, so that the given pre-processing transformation is applied to the subset of the images prior to the analysis using the machine-learning model. 19. The computer-readable storage medium of claim 17, wherein the operations further comprise: determining the image context after receiving the instructions, so that the given pre-processing transformation is applied to the subset of the images prior to the analysis using the machine-learning model. 20. A method for determining whether a machine-learning model is operating within predefined limits, the method comprising: receiving, from another electronic device, instructions for the machine-learning model, a reference input and a predetermined output of the machine-learning model for the reference input, wherein the instructions including an architecture of the machine-learning model, weights associated with the machine-learning model and a set of pre-processing transformations for use when executing the machine-learning model on images; configuring the machine-learning model based on the instructions; calculating an output of the machine-learning model for the reference input; and determining whether the machine-learning model is operating within the predefined limits based on the output and the predetermined output.