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Manage models

To access a list of available Machine Learning (ML) models, select AutoML > Models. Control Tower updates the list every time a new model is deployed to your environment, or you complete a training run.

From the list, you can also find out the following information about each available ML model:

Column	Description
ID	Model identifier specified by users in the course of model training.
Code	Pipeline identifier specified in its source code description.
Version	Pipeline version specified in its source code description.
Imported	Date and time (in the dd.mm.yyyy, hh:mm format) when the model was added to your environment. New models appear in an environment either via explicit import operations or after training.
Type	Problem type the model can solve (for instance, information extraction or classification).
P	Precision of the model calculated based on training results.
R	Recall of the model calculated based on training results.
Accuracy	Accuracy rate for the model calculated based on training results.

View model details

To view the detailed data about a particular model, click its ID in the list or open its Actions menu and select View.

The displayed page contains all or any of the following data items:

• Code
• Version
• Import timestamp (dd.mm.yyyy, hh:mm)
• Type
• Environment
• Precision, recall, and accuracy calculations
• Data point statistics

To export the data point statistics (if available), click Export as CSV.

Filter and search

You can filter the list of displayed models based on any or all the three criteria:

• Model's pipeline code
• Pipeline version
• Model type

To apply the criteria, click one or more filter buttons at the top of the model list and select required items or find them using the search box. Filters turn blue as they become active, and the items in the model list change accordingly to show the filtering results.

To turn off an active filter, click it again. To reset them all, click Clear all.

You can also search the list for particular models using the Search by ID and code box in the top-right corner.

Create model

You can create a model in one of the two ways via the AutoML UI in Control Tower:

• Train a new model.

• Create a model without training.

Train new model

To train a new model based on an available pipeline, follow the steps below:

1. In the top-right corner of the Models page, click the plus (+) button to open the menu and select Train new.

   

2. To create a model from scratch, click Train.

   

3. Fill in the fields to configure a pipeline for the new model and click Next.

   

   After clicking Next, the training flow steps change on the right, depending on the pipeline you specified. The completed step is marked with a green tick as shown below:

   

4. On the Testing step, choose a testing method. If you select Test set, specify one in the appropriate field. As you start to type in the field, available options appear as a drop-down list.

   

   To proceed to the next step, click Next.

5. Configure labels by switching toggle buttons on or off. The initial list of labels is fetched from the selected dataset, and the initial configuration—from the model's pipeline.

   

   caution

   The step is not available for classification models.

6. For each required label, configure normalizers and click Next to continue. To access normalizers, click the arrow button next to a specific label.

   

   The initial normalizer configuration is fetched from the model's pipeline.

7. Configure post-processors (if any) by switching toggles on and off and click Next to proceed.

   

   The initial post-processor configuration displayed on the page is fetched from the model's pipeline.

8. Configure the decision rules (if any) as appropriate for your process. The configuration definition is received from the model's pipeline.

   

9. Once you are done with the configuration, click Start to start the model training.

10. Navigate to AutoML > Experiments. On the Trainings tab, find the new model training run: its status should be In progress.

    

11. Wait till the status changes to Completed. Then, click the training ID and review the resulting model data.

    

    Alternatively, to access the model details, open the Actions menu and select View.

Modify model

The modifying flow lets you update configurations of existing ML models without training them. As a result, you get a new artifact with an updated configuration.

Start flow

There are two methods how you can start the modifying flow:

• Via the the Model page menu > Train new > Modify > Update the configuration

  

• Via Actions > Copy to modify > Update the configuration

  

  When you start the flow from copying, mind that it comprises two stages:

  1. You select a model to update and make a copy of it.

  2. You launch a test for the copied model.

  If the copying stage fails, a model is not created. When the test stage fails, a model artifact is created, but the statics is not available.

Update configuration

In the Modify dialog, follow the steps to create a model:

1. Select a prototype to modify and specify the new model ID. If you start from Copy to modify, the prototype is already selected for you: it's the model you copied.

   

2. Click Next to proceed. On the right, you will see more steps appear for the modifying flow. The completed step is marked with a green tick.

3. Select a test set and click Next.

   

4. Configure labels by switching the toggles on and off. The initial label configuration is fetched from the prototype model.

   

5. For each required label, configure normalizers and click Next to continue. To access normalizers, click the arrow button next to a specific label.

   

   The initial normalizer configuration is fetched from the prototype model.

6. Configure post-processors (if any) by switching the toggles on or off. The displayed list of post-processors is fetched from the prototype model.

   

7. Configure decision rules. They are available if the prototype model contains their definition.

   

8. Click Start.

As a result, a new artifact is created, and a test is launched. To find the test, navigate to AutoML > Experiments > Test. Test results are stored in the model's meta-info.

Test model

To test a model, do as follows:

1. Go to Models and select the model you want to test.

2. Click the Actions menu and select Test.

   

3. Proceed with the testing flow as described in Run experiments | Start test. To view test run details, refer to Run experiments | View test details.

Import model

To import a model as a bundle (a zip archive) to your environment, open the menu in the top-right corner of the Models page and select Import.

Alternatively, you can import a bundle via the Pipeline page.

important

An imported bundle can include only models and pipelines. When it contains any other assets, the import fails.

In case you import models only, associated pipelines must be already deployed to the target environment. Otherwise, the import fails.

The import status is visible above the Model list. The following statuses are possible:

• Uploading: the bundle is being uploaded to Control Tower. At the stage, you can stop the procedure by clicking the Stop (cross-shaped) button.

  

• Importing: models and pipelines are being deployed to your environment. At the stage, you can no longer stop the procedure.

  

• Success: the status appears once the assets from the bundle are moved to the target environment. It disappears automatically after about 15 seconds, or you can click the close (cross) button. To view the bundle details, click View.

  

• Failed: the status appears for each error. You need to close the messages manually. Those assets that were imported before the error (if any) appear in the Model and Pipeline lists accordingly.

  

Export model

You can export a model as a bundle (a zip archive) in one of the two ways:

• Open the Actions menu and select Export model.

  

• Open the model's data page and click Export.

  

The resulting zip bundle contains a model (training output) and a pipeline (artifact).