Abstract: Systems and methods for generating geolocation-based images for a target object are provided. A geolocation module receives a set of geolocations associated with a geographic region of interest. Each geolocation of the set of geolocations is mapped to context data associated with the geolocation. A prompt generation module generates multiple prompts based on the set of geolocations and the context data. The prompt generation module comprises a first generative artificial intelligence (AI) model. An image generation module generates multiple synthetic images based on the multiple prompts. The image generation module comprises a second generative AI model. Each synthetic image depicts the target object in a background generated based on a prompt.

Abstract: Methods and systems are provided for using machine learning to efficiently promote eco-friendly products. In embodiments described herein, a product descriptions associated with a product is obtained. The product description includes subject matter indicating an environmental effect of the product. Thereafter, a score for the product correlated to the environmental effect of the product is generated by a machine learning model based on the product description of the product. The score is then provided for presentation to a user to indicate the correlated environmental effect of the product.

Abstract: Systems and methods for inferring compatibility relationships are described. Embodiments of the present disclosure identify user interaction history including an interaction between a user and a first product, wherein the first product comprises an attribute that is compatible with a subset of available products; query a database that includes the available products to identify a second product from the subset of available products based on the attribute, wherein the second product is identified based on a knowledge graph that includes a first node representing the first product and a second node representing the second product; and provide a customized user experience for the user that indicates the second product and the attribute.

Abstract: A system includes a device management infrastructure arranged to process a set of training data using a data transformation model to generate first characteristic data indicative of values of one or more characteristics for the training data, and an electronic device communicatively coupled to the device management infrastructure. The device includes memory circuitry arranged to store a machine learning model trained using the set of training data, and a copy of the data transformation model. The device is arranged to process a set of input data using the data transformation model to generate second characteristic data indicative of values of said set of data characteristics for the input data. The device and/or the device management infrastructure is arranged to determine whether the first characteristic data and the second characteristic data satisfy one or more consistency criteria indicative of consistency between the training data and the input data.

Abstract: A method performed by an electronic device for testing machine learning models is described. The electronic device includes a program for executing a first machine learning model and a second machine learning model. The electronic device receives a machine learning model update data package and generates the second machine learning model from the update data package by partially or fully updating the first machine learning model. When the program is executed, both the first and second machine learning model use a common set of input data to perform inference. The program collects outputs from the first machine learning model and the second machine learning model, for further analysis.