Hub

19 results

Filter
Deployment
Fraud Fraud detection Banking Anomaly detection Credit card Cybersecurity Random forest Keras Autoencoder
  1. Workflow
    Training Workflow Fraud Detection
    Integrated deployment Continous Model monitoring
    +4
    In this simple example a Random Forest model has been trained to detect potential Fraud Transactions. To train the model it has b…
    knime > Continuous Deployment for Data Science > 03_Examples > Fraud Detection > Level_3_Fraud Detection with Monitoring > Training > Training_workflow
    0
    knime
  2. Workflow
    Fraud Detection: Model Training
    Fraud Fraud detection Random forest
    +5
    This workflow reads in the creditcard.csv file and trains and evaluates a Random Forest model to classify transactions as either …
    knime > Examples > 50_Applications > 39_Fraud_Detection > 01_Fraud_Detection_Model_Training
    0
    knime
  3. Workflow
    Fraud Detection: Model Deployment
    Fraud Fraud detection Random forest
    +5
    This workflow, the deployment workflow, reads the trained model, as well as the new transaction and applies the model to classify…
    knime > Examples > 50_Applications > 39_Fraud_Detection > 02_Fraud_Detection_Deployment
    1
    knime
  4. Workflow
    Training Workflow Fraud Detection
    Integrated deployment Continous Model monitoring
    +4
    In this simple example a Random Forest model has been trained to detect potential Fraud Transactions. To train the model it has b…
    fitofeijoo > Public > 03_Examples > Fraud Detection > Level_3_Fraud Detection with Monitoring > Training > Training_workflow
    0
    fitofeijoo
  5. Workflow
    Keras Autoencoder for Fraud Detection Deployment
    Autoencoder Keras Neural network
    +16
    Read Keras model. Read deployment data, which are normalized into range [0,1]. Apply the Keras model to the deployment data, calc…
    knime > Examples > 50_Applications > 39_Fraud_Detection > 04_Keras_Autoencoder_for_Fraud_Detection_Deployment
    1
    knime
  6. Workflow
    Isolation Forest for Fraud Detection: Model Deployment
    Fraud detection Deployment Isolation forest
    +5
    This workflow reads the trained isolation forest model, as well as the new transaction and applies the model to it. Based on the …
    knime > Examples > 50_Applications > 39_Fraud_Detection > 06_Isolation_Forest_for_Fraud_Detection_Deployment
    0
    knime
  7. Workflow
    Fraud Detection: Model Deployment
    Fraud Fraud detection Random forest
    +1
    This workflow, the deployment workflow, reads the trained model, as well as the new transaction and applies the model to classify…
    kathrin > Public > Faud_Detection_Autoencoder > Isolation_Forest_for_Fraud_Detection_Deployment
    0
    kathrin
  8. Workflow
    Keras Autoencoder for Fraud Detection - Deployment
    Autoencoder Keras Neural network
    +16
    Exercise of the L4-DL Introduction to Deep Learning Course. The goal of this exercise is to apply a trained autoencoder to new tr…
    knime > Education > Courses > L4-DL Introduction to Deep Learning > Session2 > Exercises > 02_Fraud_Detection_Deployment_Exercise
    0
    knime
  9. Workflow
    Keras Autoencoder for Fraud Detection - Deployment
    Autoencoder Keras Neural network
    +16
    Exercise of the L4-DL Introduction to Deep Learning Course. The goal of this exercise is to apply a trained autoencoder to new tr…
    knime > Education > Courses > L4-DL Introduction to Deep Learning > Session2 > Solutions > 02_Fraud_Detection_Deployment_Solution
    0
    knime
  10. Workflow
    Fraud Detection: JSON Input
    Fraud Fraud detection Random forest
    +6
    This workflow showcases how the Container Input (JSON) and Container Output (JSON) nodes can be used to create a REST API for a w…
    jtyler > KNIME-Edge-Server-Workflows > Example_Use_Cases > General > Fraud Detection > 04_Fraud_Detection_JSON_Input
    0
    jtyler
  11. Workflow
    Fraud Detection: Table Input
    Fraud Fraud detection Random forest
    +6
    This workflow showcases how the Container Input (Table) and Container Output (Table) nodes can be used to create a REST API for a…
    jtyler > KNIME-Edge-Server-Workflows > Example_Use_Cases > General > Fraud Detection > 02_Fraud_Detection_Table_Input
    0
    jtyler
  12. Workflow
    Fraud Detection: Row Input
    Fraud Fraud detection Random forest
    +6
    This workflow showcases how the Container Input (Row) and Container Output (Row) nodes can be used to create a REST API for a wor…
    jtyler > KNIME-Edge-Server-Workflows > Example_Use_Cases > General > Fraud Detection > 01_Fraud_Detection_Row_Input
    0
    jtyler
  13. Workflow
    Fraud Detection: Row and Variable Input
    Fraud Fraud detection Random forest
    +6
    This workflow showcases how the Container Input (Row), Container Input (Variable) and Container Output (JSON) nodes can be used t…
    jtyler > KNIME-Edge-Server-Workflows > Example_Use_Cases > General > Fraud Detection > 03_Fraud_Detection_Row_And_Variable_Input
    0
    jtyler
  14. Workflow
    Keras Autoencoder for Fraud Detection - Integrated Deployment Call
    Autoencoder Keras Neural network
    +16
    This workflow executes the model generated by the Integrated deployment to get a prediction of fraudolent transaction.
    knime > Education > Courses > L4-DL Introduction to Deep Learning > Supplementary workflows > Autoencoder > 02_Fraud_Detection_Call
    0
    knime
  15. Workflow
    Keras Autoencoder for Fraud Detection - Deployment
    Autoencoder Keras Neural network
    +15
    This workflow applies a trained autoencoder model to detect fraudulent transactions.
    kathrin > Codeless Deep Learning with KNIME > Chapter 5 > 02_Autoencoder_for_Fraud_Detection_Deployment
    1
    kathrin
  16. Workflow
    Anomaly Detection. Time Series AR Deployment
    Anomaly detection Time series analysis Auto-regressive models
    +6
    This workflow deploys a previously trained auto-regressive model for anomaly detection: - Select the date for deployment. Two mon…
    knime > Codeless Time Series Analysis with KNIME > Chapter 11 > 03a_Time_Series_AR_Deployment
    0
    knime
  17. Workflow
    Anomaly Detection. Time Series AR Deployment
    Anomaly detection Time series analysis Auto-regressive models
    +7
    This workflow deploys a previously trained auto-regressive model for anomaly detection: - Select the date for deployment. Two mon…
    knime > Examples > 50_Applications > 17_AnomalyDetection > 03a_Time_Series_AR_Deployment
    0
    knime
  18. Workflow
    Anomaly Detection. Time Series AR Deployment
    Anomaly detection Time series analysis Auto-regressive models
    +7
    This workflow deploys a previously trained auto-regressive model for anomaly detection: - Select the date for deployment. Two mon…
    knime > Examples > 50_Applications > 40_Anomaly_Detection > 03_Time_Series_AR_Deployment
    3
    knime
  19. Workflow
    Anomaly Detection. Time Series AR Deployment
    Anomaly detection Time series analysis Auto-regressive models
    +6
    This workflow deploys a previously trained auto-regressive model for anomaly detection: - Select the date for deployment. Two mon…
    linh > Public > Time Series > 03a_Time_Series_AR_Deployment
    0
    linh