# MLOps for edge AI: Preparing AI models for deployment at the edge

DevFeed: [MLOps for edge AI: Preparing AI models for deployment at the edge](<https://devfeed.tech/articles/mlops-for-edge-ai-preparing-ai-models-for-deployment-at-the-edge-64818.md>)

Original publisher: [Read original article](<https://www.redhat.com/en/blog/mlops-edge-ai-preparing-ai-models-deployment-edge>)

Author: Luis Javier Arizmendi Alonso

Published: 2026-10-05T00:00:00Z

Content type: tutorial

Language: en

Sources: [Red Hat Blog](<https://devfeed.tech/sources/red-hat-blog.md>)

Topics: [MLOps](<https://devfeed.tech/topics/mlops.md>), [AI software supply chain security](<https://devfeed.tech/topics/ai-software-supply-chain-security.md>), [Deployment Strategies](<https://devfeed.tech/topics/deployment-strategies.md>), [Post-training optimization](<https://devfeed.tech/topics/post-training-optimization.md>), [TensorRT](<https://devfeed.tech/topics/tensorrt.md>), [llama.cpp](<https://devfeed.tech/topics/llama-cpp.md>)

Tags: [access-control](<https://devfeed.tech/tags/access-control.md>), [ai-bill-of-materials](<https://devfeed.tech/tags/ai-bill-of-materials.md>), [aibom](<https://devfeed.tech/tags/aibom.md>), [air-gapped](<https://devfeed.tech/tags/air-gapped.md>), [application](<https://devfeed.tech/tags/application.md>), [authentication](<https://devfeed.tech/tags/authentication.md>), [backdoor](<https://devfeed.tech/tags/backdoor.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [edge-ai](<https://devfeed.tech/tags/edge-ai.md>), [here](<https://devfeed.tech/tags/here.md>), [mlops](<https://devfeed.tech/tags/mlops.md>), [use-cases](<https://devfeed.tech/tags/use-cases.md>)

## AI overview

This article describes an edge AI MLOps workflow from model preparation to deployment. It covers model optimization and runtime formats, CI/CD validation, packaging and distributing models through container registries, and supply chain security measures such as signing, SBOMs, and secure boot.

## Source excerpt

When I started playing with AI models for edge computing use cases, everything went smoothly. But once I reached the "it works" point and started thinking about how to operationalize it, the whole thing became more complicated. I started to question how a trained model becomes something you can ship to the edge. How can I get the most out of edge devices while running AI models? How can I manage hundreds of distributed models? How can I maintain accuracy over time?If you've ever had these questions, you should continue reading this article series. Here, we'll answer these and other questions r