Provided is a system and method for training and validating models in a machine learning pipeline for failure mode analytics. The machine learning pipeline may include an unsupervised training phase, a validation phase and a supervised training and scoring phase. In one example, the method may include receiving an identification of a machine learning model, executing a machine learning pipeline comprising a plurality of services which train the machine learning model via at least one of an unsupervised learning process and a supervised learning process, the machine learning pipeline being controlled by an orchestration module that triggers ordered execution of the services, and storing the trained machine learning model output from the machine learning pipeline in a database associated with the machine learning pipeline.

Embodiments of this application disclose a training method for a semi-supervised learning model which can be applied to computer vision in the field of artificial intelligence. The method includes: first predicting classification categories of some unlabeled samples by using a trained first semi-supervised learning model, to obtain a prediction label; and determining whether each prediction label is correct in a one-bit labeling manner, and if prediction is correct, obtaining a correct label (a positive label) of the sample, or if prediction is incorrect, excluding an incorrect label (a negative label) of the sample. Then, in a next training phase, a training set (a first training set) is reconstructed based on the information, and an initial semi-supervised learning model is retrained based on the first training set, to improve prediction accuracy of the model. In one-bit labeling, an annotator only needs to answer “yes” or “no” for the prediction label.

In some embodiments, an apparatus includes providing a representation of a set of digital medical images to a first machine learning model to define a feature vector associated with a presence of an intracranial hemorrhage. A representation of the set of digital medical images is provided to a second machine learning model to define a second feature vector associated with a volume of the intracranial hemorrhage. Using a third machine learning model, a set of EMRs associated with risk factors for a predefined indication is analyzed to define a third feature vector. The first, second and third feature vectors are provided as inputs to a fourth machine learning model to determine a metric associated with an applicability of a specific treatment associated with a predefined indication. An alert is sent to relevant healthcare providers and relevant tests, procedures or bloodwork are ordered for the predefined indication.

A machine learning classifier system includes a data set processing subsystem to generate a training set and a validation set from multiple data sources. Classifier hardware induces a classifier according to the training set, and tests the classifier according to the validation set. A buffer connected to the classifier hardware stores data objects to be classified, and a register connected to the classifier hardware stores outputs of the classifier, including classified data objects.

A distributed manufacturing network information technology system includes a cloud-based additive manufacturing management platform with a user interface, connectivity facilities, data storage facilities, and monitoring facilities. The distributed manufacturing network information technology system includes a set of applications for enabling the additive manufacturing management platform to manage a set of distributed manufacturing network entities. The distributed manufacturing network information technology system includes an artificial intelligence system configured to learn on a training set of outcomes, parameters, and data collected from the distributed manufacturing network entities to optimize manufacturing and value chain workflows.