When improving your assistant, you’ll make different kinds of fixes to your bot. To automate the testing and integration of these improvements into your deployed assistant, you should set up a CI/CD (Continuous Integration/Continuous Deployment) pipeline on your connected git repository.
For example, you could add a step in your pipeline that pushes a newly trained model to Rasa Enterprise everytime a change is merged into your main branch. For more information on setting up a CI/CD pipeline, check out the Rasa Open Source user guide on CI/CD.
Here are a few examples of CI/CD pipelines in Github Actions to get you started:
The rasa-demo CI/CD pipeline includes the following steps; some are conditional:
Lints and type-tests the action code
Validates the data
Runs NLU cross-validation
Trains a model
Tests the model on test conversations
Builds and tags an action image
Pushes the action image to a private Google Cloud Container Registry
This example includes some of the steps above, but with fewer conditions:
This example includes the steps above, but also includes steps to create an AWS EKS cluster and deploy the bot there: