In v39, models for flows created in v37 and above are forward compatible, meaning that you can use them in v39 without having to retrain them during the upgrade process. As a result, forward-compatible models allow you to:
upgrade v37 or v38 models after upgrading the application to v39,
maintain the automation levels achieved in v37 or v38, and
continue existing training efforts for any use cases that may be in implementation when upgrading to v39.
Which models are forward-compatible
In v39, the following types of models are forward-compatible:
Locator models
Classification models
The following types of models are not forward compatible, so you need to train v39 versions of these models in order to use them in v39:
Transcription models (i.e., sets of finetuning models)
Text Classification models
To learn more about Text Classification, see Text Classification.
Using v37 and v38 models and flows in v39
As mentioned earlier, you can continue using v37 and v38 flows in v39 without interruption or loss of automation. However, flows from older versions can only use the models they were using before upgrading to a newer version, and you cannot import models for these flows. You cannot import a v37 model for use with a v38 or v39 flow, and you cannot import a v38 model for use with a v39 flow.
If you need to change a model for a v37 or v38 flow (e.g., train it for a new Semi-structured layout), you must create a new model, along with a new flow in v39 to go with it. When changing a model, all of the models for the model's flow must be upgraded for the new v39 flow. In other words, you cannot upgrade a flow's models selectively or in a piecemeal fashion. You also cannot attach an older trainer to update the models for a v39 flow.
When using v37 or v38 flows, you can use cloned versions of the releases you used in v37 or v38. However, if you add Semi-structured layouts to those releases, there is no automation for the submissions matched to these layouts. That is, the system generates Supervision tasks for these submissions.
Memory Management
If you are using flows from multiple versions of the application and out-of-memory errors occur, you should enable the Memory Management feature. This feature assigns flows that were created in specific application versions to specific application machines in your instance.
To learn more about Memory Management, see Memory Management.
How to know when to train forward-compatible models
When you open a flow created in v37 or later in Flow Studio, the system lets you know which models for the flow need to be trained.
From there, you can click on an issue's Go to Model Management button.
If the affected model is a Locator Model, you are redirected to the Model Library. There, you can find the model mentioned in the issue's description and click on its name. On the model’s details page, you can take action to correct the issue.
If the model mentioned in the issue is a Classification or Transcription mode, clicking Go to Model Management takes you directly to the model’s details page, where you can take corrective action.
Models from v34 or earlier do not appear in the Model Library.