> 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/mlops-deployment.md). # MLOps Deployment Training is not shipping. The page is about putting a model into a place that can serve it, and it starts with Lightning for PyTorch. The same notes are in [Seldon](/ai-engineering/full-stack-and-ops.md#seldon) and [Serving models](/ai-engineering/full-stack-and-ops.md#serving-models). ## Lightning With the deployment problem named, Lightning is a framework for training and deploying PyTorch models without living in YAML and Kubernetes. [Lightning](https://lightning.ai/): a framework to train and deploy PyTorch models. Focus on the science, not the engineering. The deployment piece is Lightning Apps: a framework to build composable, reactive ML workflows. Bye YAML and Kubernetes, hello Python. The [Examples](https://lightning.ai/docs/app/stable/#build-self-contained-components) in the Lightning AI docs, from the PyTorch Lightning creators, show how to build self-contained components; the docs now present Lightning as an AI cloud to build, train, and deploy your own 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/mlops-deployment.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.