Abstract: Systems, computer program products, and methods are described herein for intelligent drift matching for unstructured data in a machine learning environment. The present invention is configured to receive an original unseen image from a computing device; initiate a convolutional encoder-decoder algorithm on the original unseen image; generate a reconstructed unseen image; generate an unseen input dataset based on at least the reconstructed unseen image; determine a reconstruction loss associated with the original unseen image; determine that the reconstruction loss associated with the original unseen image matches a first reconstruction loss associated with a first training image, wherein the first training image is associated with one or more training images; retrieve, from the data repository, a first set of parameters associated with the first training image based on at least determining the match; classify, using the first set of parameters, the original unseen image into one or more class labels.

Abstract: Systems, computer program products, and methods are described herein for intelligent multimodal classification in a distributed technical environment. The present invention is configured to retrieve one or more multimodal communications from a data repository; initiate one or more feature extraction algorithms on the one or more communication modalities to extract one or more features; generate a training dataset based on at least the one or more features extracted from the one or more communication modalities; initiate one or more machine learning algorithms on the training dataset to generate a first set of parameters; receive an unseen multimodal communication; generate an unseen dataset based on at least the unseen multimodal communication; classify, using the first set of parameters, the unseen multimodal communication into one or more class labels; and initiate an execution of one or more actions on the unseen multimodal communication based on at least the classification.

Abstract: Systems, computer program products, and methods are described herein for intelligent drift matching for unstructured data in a machine learning environment. The present invention is configured to receive an original unseen image from a computing device; initiate a convolutional encoder-decoder algorithm on the original unseen image; generate a reconstructed unseen image; generate an unseen input dataset based on at least the reconstructed unseen image; determine a reconstruction loss associated with the original unseen image; determine that the reconstruction loss associated with the original unseen image matches a first reconstruction loss associated with a first training image, wherein the first training image is associated with one or more training images; retrieve, from the data repository, a first set of parameters associated with the first training image based on at least determining the match; classify, using the first set of parameters, the original unseen image into one or more class labels.

Abstract: Systems, computer program products, and methods are described herein for intelligent multimodal classification in a distributed technical environment. The present invention is configured to retrieve one or more multimodal communications from a data repository; initiate one or more feature extraction algorithms on the one or more communication modalities to extract one or more features; generate a training dataset based on at least the one or more features extracted from the one or more communication modalities; initiate one or more machine learning algorithms on the training dataset to generate a first set of parameters; receive an unseen multimodal communication; generate an unseen dataset based on at least the unseen multimodal communication; classify, using the first set of parameters, the unseen multimodal communication into one or more class labels; and initiate an execution of one or more actions on the unseen multimodal communication based on at least the classification.