Machine learning model continuous training system
Described is a system for performing a set of machine learning model training operations that include: accessing media content items associated with interaction functions initiated by users of an interaction system, generating training data including labels for the media content items, extracting features from a media content item of the media content items, identifying additional media content items to include in the training data based on the extracted features from the media content item, processing the training data using a machine learning model to generate a media content item output; and updating one or more parameters of the machine learning model based on the media content item output. The system checks whether retraining criteria has been met, and repeats the set of machine learning model training operations to retrain the machine learning model.
1 . A system comprising:
at least one processor;
at least one memory component storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising:
training a machine learning model by performing a set of operations comprising:
accessing media content items associated with interaction functions initiated by users of an interaction system, wherein the media content items comprise images, videos, or content augmentations of the users posted on the interaction system enabling other users to view the posted media content items;
generating training data including labels for the media content items, wherein the labels are indicative of one or more characteristics of the media content items;
extracting features from a media content item of the media content items;
identifying additional media content items to include in the training data based on the extracted features from the media content item;
processing the training data using a machine learning model to generate a media content item output; and
updating one or more parameters of the machine learning model based on the media content item output; and
repeating the set of operations to retrain the machine learning model based on a retraining criterion being met.
2 . The system of claim 1 , wherein identifying additional media content items comprises applying a distance metric to compare the media content item and individual additional media content items in order to identify the additional media content items.
3 . The system of claim 2 , wherein extracting the features from the media content item comprises applying a machine learning model trained to extract features from one or more media content items.
4 . The system of claim 3 , wherein generating the training data comprises adding the one or more extracted features to the labels.
5 . The system of claim 2 , wherein the media content items comprise videos created by users to share with other users, wherein the features are extracted on a frame-by-frame basis.
6 . The system of claim 2 , wherein the media content items comprise content augmentations created by users to share with other users, wherein the extracted features include the augmentations that are applied to a camera feed in real-time.
7 . The system of claim 2 , wherein at least some of the media content items are in a different format than the additional media content items.
8 . The system of claim 7 , wherein the media content items that are in the different format are compared with the additional media contents based on the extracted features.
9 . The system of claim 1 , wherein the additional media content items were created by users in a different time period than when the accessed media content items were created.
10 . The system of claim 1 , wherein the additional media content items were created by different users than the users that created the accessed media content items.
11 . The system of claim 1 , wherein the additional media content items are identified based on metadata of the accessed media content items.
12 . The system of claim 11 , wherein the metadata comprises a location where a user created the individual media content item.
13 . The system of claim 1 , wherein the retraining criterion comprises a keyword indicative of a trend, wherein the operations further comprise tracking the use of one or more keywords in media content items and the retraining criterion includes meeting a threshold number of uses of the keyword.
14 . The system of claim 1 , wherein the media content items comprise images or videos, and the interaction functions comprise media content items created by users and shared with other users.
15 . The system of claim 14 , wherein the media content items that were created by users do not include labels for training the machine learning model, wherein generating the training data includes identifying keywords in captions of individual media content items or comments to the media content items from other users.
16 . The system of claim 14 , wherein the media content items comprise content augmentations that add interactive digital elements in real-time to a camera feed.
17 . The system of claim 1 , wherein the operations further comprise adding the training data to an existing set of training data, wherein repeating the set of operations further comprises adding newly accessed training data to the existing set of training data such that the existing set of training data increases in size with each repeating of the set of operations.
18 . The system of claim 17 , wherein processing the training data using the machine learning model to generate the media content item output further comprises processing the existing set of training data using the machine learning model.
19 . A method comprising:
training a machine learning model by performing a set of operations comprising:
accessing media content items associated with interaction functions initiated by users of an interaction system, wherein the media content items comprise images, videos, or content augmentations of the users posted on the interaction system enabling other users to view the posted media content items;
generating training data including labels for the media content items, wherein the labels are indicative of one or more characteristics of the media content items;
extracting features from a media content item of the media content items;
identifying additional media content items to include in the training data based on the extracted features from the media content item;
processing the training data using a machine learning model to generate a media content item output; and
updating one or more parameters of the machine learning model based on the media content item output; and
repeating the set of operations to retrain the machine learning model based on a retraining criterion being met.
20 . A non-transitory computer-readable storage medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising:
training a machine learning model by performing a set of operations comprising:
accessing media content items associated with interaction functions initiated by users of an interaction system, wherein the media content items comprise images, videos, or content augmentations of the users posted on the interaction system enabling other users to view the posted media content items;
generating training data including labels for the media content items, wherein the labels are indicative of one or more characteristics of the media content items;
extracting features from a media content item of the media content items;
identifying additional media content items to include in the training data based on the extracted features from the media content item;
processing the training data using a machine learning model to generate a media content item output; and
updating one or more parameters of the machine learning model based on the media content item output; and
repeating the set of operations to retrain the machine learning model based on a retraining criterion being met.