It’s easy to get lost in logs and dashboards while getting to the root of build or test failures. By leveraging the data made available by Kubernetes testing and visualization platforms like Prow and TestGrid, we have built AI4CI (Artificial Intelligence for Continuous Integration), an intelligent open source AIOps toolkit which can be used to better monitor builds to help developers get to the root cause of failures. AI4CI collects data from various Kubernetes CI/CD tools to calculate key performance indicator metrics. These metrics can help monitor the state of a CI workflow and can be shared via automated dashboards running on Kubeflow pipelines which can help investigate problematic tests, builds, or jobs.
Starting with this open source AIOps toolkit, there is a focus on cultivating an open source community which uses open operations data and an open infrastructure for data scientists and DevOps engineers to collaborate.
In this session, the speakers demonstrate some example ML use-cases, share dashboards, getting-started guides, and jupyter notebooks which attendees can easily get started with, to evaluate the current state of their CI workflow.
By the end of this session, attendees learn how to:
- Use open source AIOps tools to monitor their CI/CD workflows.
- Leverage dashboards to get more visibility into build failures and root cause analysis.
- Speed up the development lifecycle by building smarter testing and visualization platforms.