Here, we define some terms used in Openlayer.


When you create an account, you are given a workspace. A workspace is your home in Openlayer, where all your projects live and where your team collaborates.


A project lives inside a workspace. It represents a problem you are tackling with AI/ML and houses all the models, data, and tests related to it. You can create multiple projects in your workspace, and your team members have access to all of them.

A project has two modes: development and monitoring.

Development mode

The development mode of a project hosts your efforts during model development. It is where you push models and datasets for testing and keep track of their versions. Refer to the Development overview for a deep dive into the process followed in development.

Monitoring mode

The monitoring mode of a project allows you to monitor a model deployed in production. It receives production data and evaluates it with the tests you defined. In the monitoring mode, you can also set up alerts to get notified when tests start failing. Refer to the Monitoring overview for details.


Tests materialize expectations around models and data and exist to measure different aspects of quality and performance.

In development, they ensure you are systematically making progress and avoiding regressions in your quest toward high-quality models. In monitoring, they help measure the model’s health in production and trigger notifications when they fail. Refer to the Tests overview and to the Understanding tests guides to learn more about them.

Inference pipeline

The inference pipeline is part of a project’s monitoring mode. It represents a model that is deployed in production making inferences.

A common setup for many teams is to have two inference pipelines: one named staging, and the other production. When you publish/stream production data to Openlayer, you need to specify which inference pipeline it belongs to.