This workflow reads in the creditcard.csv file and trains and evaluates a Random Forest model to classify transactions as either …

This workflow, the deployment workflow, reads the trained model, as well as the new transaction and applies the model to classify…

Read Keras model. Read deployment data, which are normalized into range [0,1]. Apply the Keras model to the deployment data, calc…

This workflow reads the trained isolation forest model, as well as the new transaction and applies the model to it. Based on the …

Exercise of the L4-DL Introduction to Deep Learning Course. The goal of this exercise is to apply a trained autoencoder to new tr…

Exercise of the L4-DL Introduction to Deep Learning Course. The goal of this exercise is to apply a trained autoencoder to new tr…

This workflow showcases how the Container Input (Table) and Container Output (Table) nodes can be used to create a REST API for a…

This workflow showcases how the Container Input (JSON) and Container Output (JSON) nodes can be used to create a REST API for a w…

This workflow showcases how the Container Input (Row) and Container Output (Row) nodes can be used to create a REST API for a wor…

This workflow executes the model generated by the Integrated deployment to get a prediction of fraudolent transaction.

This workflow showcases how the Container Input (Row), Container Input (Variable) and Container Output (JSON) nodes can be used t…

This workflow applies a trained autoencoder model to detect fraudulent transactions.