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CI/CD for AI model deployments

baseten.co · 936 words · saved by 2 readers

Continuous integration and continuous deployment (CI/CD) is an industry standard for everything from web to mobile to API development. But for AI models, deployment is often a manual process. What would CI/CD look like for AI models? Broadly, AI models need a pipeline that looks like any other CI/CD setup: A safe environment to deploy model updates without affecting production. A way to test newly deployed models before promoting to production. A way to seamlessly transition traffic from an old production deployment to a new one. A way to revert to a previous production deployment if there are any issues. At least the first three steps should be fully automated, and any reversions should be quick and seamless. Below, we’ll walk through a reference architecture for CI/CD for AI models using Baseten’s model management and model inference APIs. Baseten uses Truss, our open source model packaging framework, for model deployment. Every deployment starts with: By default, this creates a deve

Continuous integration and continuous deployment (CI/CD) is an industry standard for everything from web to mobile to API development. But for AI models, deployment is often a manual process. What would CI/CD look like for AI models? Broadly, AI models need a pipeline that looks like any other CI/CD setup: A safe environment to deploy model updates without affecting production. A way to test newly deployed models before promoting to production. A way to seamlessly transition traffic from an old production deployment to a new one. A way to revert to a previous production deployment if there are

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