Abstract: Provided are techniques for identifying and correcting model focus drift during model training. A model is trained using an original dataset with data, and the is classified into clusters. A representation is assigned to each of the clusters. A first visualization dashboard with visualizations is generated, where each visualization represents a first data distribution of an associated cluster using the representation assigned to that cluster. The model is fine-tuned using a fine-tune dataset. A second visualization dashboard is generated by updating each visualization, where each visualization represents a second data distribution of the associated cluster. It is determined that a cluster of the clusters has focus drift based on changes between the first data distribution and the second data distribution. The focus drift is corrected by: adding the data of the cluster to the fine-tune dataset to form a combined dataset and fine-tuning the model using the combined dataset.

Abstract: A method includes performing at least the following operations by one or more processors: receiving a request initiated by a device of a first user for a first service to be provisioned by a first entity, deriving a first user preference data based, at least in part, on a historical interaction information of the first user with a second entity provisioning a second service, and responsive to receiving the request, provisioning an instantiation of the first service, by the first entity, in a manner that is based, at least in part, on the first user preference data.

Abstract: A method includes performing at least the following operations by one or more processors: receiving a request initiated by a device of a first user for a first service to be provisioned by a first entity, deriving a first user preference data based, at least in part, on a historical interaction information of the first user with a second entity provisioning a second service, and responsive to receiving the request, provisioning an instantiation of the first service, by the first entity, in a manner that is based, at least in part, on the first user preference data.