> For the complete documentation index, see [llms.txt](https://www.mlcompendium.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://www.mlcompendium.com/ai-engineering/full-stack-and-ops-1/continuous-integration.md). # Continuous Integration Code has to land continuously, which means every change is built and tested automatically. The page starts with the CI products and a GitHub Actions example, then moves to the Argo courses that carry the same delivery path into Kubernetes. The same notes are in [Continuous integration](/ai-engineering/full-stack-and-ops.md#continuous-integration), [MLOps Course](/ai-engineering/mlops-course.md), and [Model Testing](/evals/data-and-model-tests.md#model-testing). The first choice is which CI service runs those builds. [Travis](https://travis-ci.org/) presents itself as the most simple and flexible CI/CD tool for continuous integration and continuous delivery. [Circle CI](https://circleci.com/) promises to deliver production-ready software at AI speed, validating, testing, and shipping every change with automation. [TeamCity](https://www.jetbrains.com/teamcity/) is the CI/CD solution by JetBrains. [Jenkins](https://www.jenkins.io/) is the open source automation server that developers use to reliably build, test, and deploy their software. GitHub Actions is the option that lives next to the code, and [poetry black pytest](https://medium.com/@vanflymen/blazing-fast-ci-with-github-actions-poetry-black-and-pytest-9e74299dd4a5) is a blazing fast CI example with GitHub Actions, Poetry, Black, and Pytest, built on a new Django project from scratch. ## Argo courses CI ends where deployment begins, and on Kubernetes Argo covers that next stretch. The five Argo videos are all by DevOps & AI Toolkit and build on each other. [Events](https://www.youtube.com/watch?v=sUPkGChvD54\&list=PLyicRj904Z9_dGuNs6AN5Khljjn9ssbQ6) is Argo Events, the event-based dependency manager for Kubernetes. [Workflows & Pipelines](https://www.youtube.com/watch?v=UMaivwrAyTA) is Argo Workflows and Pipelines for CI/CD, machine learning, and other Kubernetes workflows. [Argo CD](https://www.youtube.com/watch?v=vpWQeoaiRM4) applies GitOps principles to manage a production environment in Kubernetes. [Rollouts](https://www.youtube.com/watch?v=84Ky0aPbHvY) is Argo Rollouts, canary deployments made easy in Kubernetes. [How to harness all of the above together](https://www.youtube.com/watch?v=XNXJtxkUKeY) is "Automation of Everything", combining Argo Events, Workflows & Pipelines, CD, and Rollouts. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://www.mlcompendium.com/ai-engineering/full-stack-and-ops-1/continuous-integration.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.