Continuous Delivery in Cloud Platforms of Machine Learning Models with Human in the Loop
A system monitors execution of a machine learning model in an environment, for example, development environment or production environment. The system receives a training dataset and a production dataset. The system initializes a review dataset based on elements of the training dataset. The system samples a subset of elements of the production dataset by identifying elements from the production dataset based on their distance from elements of the review dataset. The system sends elements of the review dataset for presentation via a user interface for receiving user feedback indicating accuracy of the result of execution of the machine learning model. The execution of the machine learning model is monitored to make determination regarding deployment of the model in a production environment for continuous delivery of the model or for evaluation or quality assurance of model executing in an environment.
1 . A computer-implemented method for monitoring execution of machine learning models, the method comprising:
receiving a machine learning model trained using a training dataset;
initializing a review dataset based on elements of the training dataset;
receiving a production dataset based on values received from a production environment, wherein the machine learning model is being executed in the production environment;
sampling a subset of elements of the production dataset, the sampling comprising, repeatedly performing:
identifying an element from the production dataset that maximizes a measure of minimum distance of the element of the production dataset from elements of the review dataset, and
adding the identified element to the review dataset;
selecting one or more elements of the review dataset that were not obtained from the training dataset; and
sending the one or more elements selected from the review dataset for presentation via a user interface, the user interface configured to present a result of execution of the machine learning model for each element of the review dataset and receive user feedback indicating accuracy of the result of execution of the machine learning model.
2 . The computer-implemented method of claim 1 , wherein the one or more elements selected from the review dataset are prioritized for presentation via the user interface, wherein a priority of a sample is determined based on an order in which the sample was added to the review dataset.
3 . The computer-implemented method of claim 2 , wherein a first element added to the review dataset before a second element has higher priority for presenting via the user interface compared to the second element.
4 . The computer-implemented method of claim 1 , wherein the measure of minimum distance represents a minimum of values representing distances between a feature vector representing an element of the production dataset and a feature vector representing an element of the review dataset.
5 . The computer-implemented method of claim 1 , wherein each element of the production dataset is an image represented as a feature vector, wherein the feature vector includes:
(1) one or more global features describing the image, and
(2) one or more local features describing a portion of the image.
6 . The computer-implemented method of claim 1 , wherein the machine learning model is a convolutional neural network configured to process an image and each element of a dataset includes an image.
7 . The computer-implemented method of claim 1 , further comprising:
comparing information received in the user feedback with a result of execution of the machine learning model to evaluate the machine learning model.
8 . The computer-implemented method of claim 7 , further comprising:
responsive to the evaluation of the machine learning model indicating that the machine learning model has a quality below a threshold level, sending a request for re-training the machine learning model.
9 . A non-transitory computer readable storage medium storing instructions that when executed by a computer processor, cause the computer processor to perform steps comprising:
receiving a machine learning model trained using a training dataset;
initializing a review dataset based on elements of the training dataset;
receiving a production dataset based on values received from a production environment, wherein the machine learning model is being executed in the production environment;
sampling a subset of elements of the production dataset, the sampling comprising, repeatedly performing:
identifying an element from the production dataset that maximizes a measure of minimum distance of the element of the production dataset from elements of the review dataset, and
adding the identified element to the review dataset;
selecting one or more elements of the review dataset that were not obtained from the training dataset; and
sending the one or more elements selected from the review dataset for presentation via a user interface, the user interface configured to present a result of execution of the machine learning model for each element of the review dataset and receive user feedback indicating accuracy of the result of execution of the machine learning model.
10 . The non-transitory computer readable storage medium of claim 9 , wherein the one or more elements selected from the review dataset are prioritized for presentation via the user interface, wherein a priority of an element is determined based on an order in which the element was added to the review dataset.
11 . The non-transitory computer readable storage medium of claim 10 , wherein a first element added to the review dataset before a second element has higher priority for presenting via the user interface compared to the second element.
12 . The non-transitory computer readable storage medium of claim 9 , wherein the measure of minimum distance represents a minimum of values representing distances between a feature vector representing an element of the production dataset and a feature vector representing an element of the review dataset.
13 . The non-transitory computer readable storage medium of claim 9 , wherein each element of the production dataset is an image represented as a feature vector, wherein the feature vector includes:
(1) one or more global features describing the image, and
(2) one or more local features describing a portion of the image.
14 . The non-transitory computer readable storage medium of claim 9 , wherein the machine learning model is a convolutional neural network configured to process an image and each element includes an image.
15 . The non-transitory computer readable storage medium of claim 9 , wherein the instructions further cause the computer processor for performs steps comprising:
comparing information received in the user feedback with a result of execution of the machine learning model to evaluate the machine learning model.
16 . The non-transitory computer readable storage medium of claim 15 , wherein the instructions further cause the computer processor for performs steps comprising:
responsive to the evaluation of the machine learning model indicating that the machine learning model has a quality below a threshold level, sending a request for re-training the machine learning model.
17 . A computer system comprising:
one or more computer processors; and
a non-transitory computer readable storage medium storing instructions that when executed by the one or more computer processors, cause the one or more computer processors to perform steps comprising:
receiving a machine learning model trained using a training dataset;
initializing a review dataset based on elements of the training dataset;
receiving a production dataset based on values received from a production environment, wherein the machine learning model is being executed in the production environment;
sampling a subset of elements of the production dataset, the sampling comprising, repeatedly performing:
identifying an element from the production dataset that maximizes a measure of minimum distance of the element of the production dataset from elements of the review dataset, and
adding the identified element to the review dataset;
selecting one or more elements of the review dataset that were not obtained from the training dataset; and
sending the one or more elements selected from the review dataset for presentation via a user interface, the user interface configured to present a result of execution of the machine learning model for each element of the review dataset and receive user feedback indicating accuracy of the result of execution of the machine learning model.
18 . The computer system of claim 17 , wherein the one or more elements selected from the review dataset are prioritized for presentation via the user interface, wherein a priority of an element is determined based on an order in which the element was added to the review dataset.
19 . The computer system of claim 17 , wherein each element of the production dataset is an image represented as a feature vector, wherein the feature vector includes:
(1) one or more global features describing the image, and
(2) one or more local features describing a portion of the image.
20 . The computer system of claim 17 , wherein the instructions further cause the one or more computer processors to perform steps comprising:
comparing information received in the user feedback with a result of execution of the machine learning model to evaluate the machine learning model; and
responsive to the evaluation of the machine learning model indicating that the machine learning model has a quality below a threshold level, sending a request for re-training the machine learning model.