Machine Learning Project

This page is accessed via Logistics Machine Learning > Machine Learning Project.

The Logistics Machine Learning (LML) project defines the scope of the historical data with which to train the machine learning model. A machine learning project can consist of multiple scenarios each representing a different business segment. There are two options you can use to train:

• historical shipment data: machine learning project to consider all North American shipments. Then use machine learning scenarios to narrow down the data to consider shipments with different modes such as truckload, LTL, and parcel shipments.
• historical order data (trend/seasonality in lanes, volumes etc.): machine learning project to show the most probable routes along with their corresponding probabilities

The Machine Learning Project screen consists of machine learning project details, project data, and a grid with all the machine learning scenarios associated with the project. A machine learning project can consist of multiple scenarios each containing a different sub-set of order releases or shipments. Then, you can compare training data for all of the scenarios.

Adding a Machine Learning Project

1. Enter a Project ID field. Add a meaningful ID for your project. There is a strong possibility that you will generate many projects with slightly different shipments. So having a good way to identify and differentiate between your many projects based on the ID will become very beneficial. For example, ETA_NA.
2. Enter a Project Name. Add a descriptive name for your project. For example ESTIMATED TRANSIT TIME FOR NA.
3. Enter a Description for this project that captures the purpose of this project and the scenarios involved.
4. Select a Domain Name.
5. Select an Objective of External Service or Embedded Learning. 
6. Select an Objective Model Type:
   • Planned ETA Prediction: This objective model type uses shipment-related attributes as the input to the model at the planning time or when the actual departure time at the source is known. This objective model type provides a base prediction before any in-transit tracking events are received. This objective model type is available if you selected an Objective of External Service.
   • Planned and Event based ETA Prediction: This objective model type uses planned ETA prediction (as described above) and adds in-transit tracking event information to further refine the predicted ETA. This objective model type is available if you selected an Objective of External Service.
   • Order Route Prediction: This objective model type uses historical order data (trend/seasonality in lanes, volumes, etc.) to show the most probable routes along with their corresponding probabilities. This objective model type is available if you selected an Objective of Embedded Learning.
7. When the Objective is External Service, you can optionally enter a Logic Configuration ID.
8. Optionally, add a New Machine Learning Scenario.

The Last Exported to Machine Learning field is populated when you run the Export to Machine Learning Service action. You must click Refresh in the Export to Machine Learning Services results page.

Adding Saved Queries

On the Project Data tab, you can add saved queries to pull in the required historical shipment data.

1. Select a Saved Query Type:
   • If Planned ETA Prediction: This objective model type uses shipment-related attributes as the input to the model at the planning time or when the actual departure time at the source is known. This objective model type provides a base prediction before any in-transit tracking events are received. This objective model type is available if you selected an Objective of External Service.
   • Planned and Event based ETA Prediction: This objective model type uses planned ETA prediction (as described above) and adds in-transit tracking event information to further refine the predicted ETA. This objective model type is available if you selected an Objective of External Service.
   • If Order Route Prediction: This objective model type uses order release-related attributes as the input to the model. This objective model type is available if you selected an Objective of External Service.
2. When the Objective is External Service, you can optionally enter a Logic Configuration ID.
3. Enter one or more Saved Query IDs and click Save to populate the grid.

Deleting a Machine Learning Project

The following scenarios can occur when deleting a machine learning project:

• If the project and scenarios are only in OTM (no actions have been run), the project and all scenario data associated to the project is deleted.
• If the project data exists in both OTM and OTI (the Load Data Analytics action), the project and all scenario data associated to the project is deleted. Also, the corresponding data in Logistics Machine Learning Intelligence subject area is deleted.
• If the project data exists in OTM, OTI (using the Load Data Analytics action), and IoT (using the Export to Machine Learning Services and Perform Training actions), the data is deleted from IoT first. Only when the deletion of the IoT data is successful is the project and all scenario data associated to the project is deleted. Also, the corresponding data in Logistics Machine Learning Intelligence subject area is deleted. 
  
  If the deletion of the IoT data fails, then nothing is deleted and you see an error message.