> 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/tutorials.md). # Tutorials Serving a model as an endpoint is only the first step; managing many models in production is the real job. This page holds two end-to-end tutorials for that, first A/B serving on Kubernetes and then a full training-and-serving stack. The same notes are in [Tutorials](/ai-engineering/full-stack-and-ops.md#tutorials). The first tutorial is for the moment when simply serving up endpoints is not good enough any more. [Kubernetes, scikit-learn, s2i, gcloud, and Seldon: random serving for A/B testing](https://medium.com/analytics-vidhya/manage-ml-deployments-like-a-boss-deploy-your-first-ab-test-with-sklearn-kubernetes-and-b10ae0819dfe) is Gus Cavanaugh's walkthrough of deploying a first A/B test with scikit-learn and Kubernetes, the start of model management. The same notes are in [A/B Testing](/decision-intelligence/a-b-testing.md). The second tutorial widens model management from one deployment to the whole loop of training many models and shipping the best one. [Polyaxon for training, an Argo package and deployment, and Seldon for serving](https://medium.com/analytics-vidhya/polyaxon-argo-and-seldon-for-model-training-package-and-deployment-in-kubernetes-fa089ba7d60b) is Daniel Rodriguez's version of that loop in Kubernetes, where model management means tooling and pipelines for data scientists to develop, deploy, measure, and improve models. --- # 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/tutorials.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.