Quickstart

First, you must create a project.

Navigate and log into Openlayer. Click the “plus” button next to “Projects,” on the sidebar, to create a new project.

The project is the place on the Openlayer platform that will house your models, data, and tests.

Select a template on the modal that appears. You can pattern-match based on the programming language, the framework, the AI/ML task type, or the use case.

Not sure which one to use? How about OpenAI in Python?

You must connect to your GitHub account after you select your template. This is important because Openlayer will clone a copy of the template into your GitHub account. Then, every new commit that includes changes to your AI system also gets pushed to Openlayer.

Make sure to provide the environment variables required to deploy the template. The example above requires an OpenAI API key, which is used to run the model on a validation set. The validation set for the templates only has three rows, so the costs are negligible.

After the GitHub repo is successfully connected, Openlayer will pull your latest commit and run a series of processing steps. These include validating your files, iterating over your datasets, and running your model to get its predictions.

In your GitHub repo, you can see that Openlayer started to run on your latest commit.

You can check out the logs on the Openlayer platform to understand the processing steps and debug any issues that arise. Once the commit finishes processing, you can move to the next step.

Tests materialize expectations around your model and data. Openlayer offers an ever-increasing suite of tests targetting different parts of your system.

After your first commit, a few general-purpose tests are automatically created. In this example, some are passing and others aren’t.

You are encouraged to collaborate with your team as you create more tests and define the success criteria for your system.

Your new commits to the Git repo will trigger your Openlayer tests.

This is how you make Openlayer part of your CI/CD system, targeting specifically the AI components of your system.

The same template repo has all the information needed to use Openlayer’s monitoring mode and log your live requests to Openlayer.

In general, you’ll only need to set a few environment variables that specify your Openlayer API key and project name.

As your system receives live requests, they are streamed to the Openlayer platform, where you can see their traces, and define tests that run periodically on top of your data.

You can set up alerts to get notified immediately if your tests start failing.

Refer to the template README and the Monitoring overview for details.