Beyond Continuous Training

Continuous Integration (CI) and Continuous Delivery (CD) are well-established practices in software engineering. In machine learning, we can adapt these practices to automate the process of building, testing, and deploying ML models.

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1. Continuous Integration (CI)

In the context of ML, CI involves automatically testing your code and your model. This includes:

• Unit tests: Testing individual functions and classes in your code.
• Integration tests: Testing how different parts of your system work together.
• Model validation: Testing your model's performance on a validation set.

You can use a CI tool like Cloud Build to automatically run these tests whenever you push a new commit to your Git repository.

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2. Continuous Delivery (CD)

CD involves automatically deploying your model to production after it has passed all the tests. This can be done in a variety of ways, such as:

• Blue/green deployment: Deploying the new model to a separate environment and then switching the traffic over to it.
• Canary deployment: Gradually rolling out the new model to a small percentage of users before making it available to everyone.
• Shadow deployment: Deploying the new model alongside the current model and comparing their predictions without affecting the user experience.

You can use a CD tool like Cloud Build to automate the deployment process.

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3. The CI/CD/CT Pipeline

A complete CI/CD/CT pipeline for ML would look something like this:

1. A developer pushes a new commit to a Git repository.
2. A Cloud Build trigger runs the CI pipeline, which builds the code, runs the tests, and validates the model.
3. If the CI pipeline is successful, a Cloud Build trigger runs the CT pipeline, which retrains the model on the latest data.
4. If the CT pipeline is successful, a Cloud Build trigger runs the CD pipeline, which deploys the model to production.

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Knowledge Check