A computer-implemented method comprising: receiving one or more requests to train an anomaly detection machine learning model using feedback-based training, the request to indicate one or more of a type of analysis to perform, a model selection indication, and a configuration for a training dataset; training the anomaly detection machine learning model according to the one or more requests using the training data; and performing feedback-based training on the trained anomaly detection machine learning model by at least: applying the trained anomaly detection machine learning model on a set of unlabeled testing data that is different than the training data to generate a prediction of whether the unlabeled testing data is anomalous, presenting a graphical user interface showing results of the prediction including at least indications of which items of the set of unlabeled testing data were determined to be anomalous, an anomaly score for each item, and the item itself; receiving user feedback on the prediction, wherein the user feedback includes an indication of a veracity of the prediction, updating the training data, comprising adding to the training data correctly predicted data of the unlabeled testing data according to the received user feedback and including a label for the correctly predicted data of the unlabeled testing data based on the received user feedback, and retraining the trained anomaly detection machine learning model using the updated training data, wherein the anomaly detection machine learning model is to look for anomalies in image data, wherein the training data comprises images, and wherein the unlabeled testing data comprises images. receiving one or more requests to train an anomaly detection machine learning model using feedback-based training, the request to indicate one or more of a type of analysis to perform, a model selection indication, and a configuration for a training dataset; training the anomaly detection machine learning model according to the one or more requests using the training data; and performing feedback-based training on the trained anomaly detection machine learning model by at least: applying the trained anomaly detection machine learning model on a set of unlabeled testing data that is different than the training data to generate a prediction of whether the unlabeled testing data is anomalous, presenting a graphical user interface showing results of the prediction including at least indications of which items of the set of unlabeled testing data were determined to be anomalous, an anomaly score for each item, and the item itself; receiving user feedback on the prediction, wherein the user feedback includes an indication of a veracity of the prediction, updating the training data, comprising adding to the training data correctly predicted data of the unlabeled testing data according to the received user feedback and including a label for the correctly predicted data of the unlabeled testing data based on the received user feedback, and retraining the trained anomaly detection machine learning model using the updated training data, applying the trained anomaly detection machine learning model on a set of unlabeled testing data that is different than the training data to generate a prediction of whether the unlabeled testing data is anomalous, presenting a graphical user interface showing results of the prediction including at least indications of which items of the set of unlabeled testing data were determined to be anomalous, an anomaly score for each item, and the item itself; receiving user feedback on the prediction, wherein the user feedback includes an indication of a veracity of the prediction, updating the training data, comprising adding to the training data correctly predicted data of the unlabeled testing data according to the received user feedback and including a label for the correctly predicted data of the unlabeled testing data based on the received user feedback, and retraining the trained anomaly detection machine learning model using the updated training data, wherein the anomaly detection machine learning model is to look for anomalies in image data, wherein the training data comprises images, and wherein the unlabeled testing data comprises images.