Deployments (Preview)
Gradient Deployments helps you perform effortless model serving.
After Notebooks and Workflows, the other major component of Gradient's end-to-end data science platform is Deployments.
A Deployment is used to run container images and to serve your machine learning models using a high-performance, low-latency micro-service with a RESTful API. This allows the model to be run on new unseen data in production, also known as model inference.

Key concepts

Deployments has a number of concepts to allow different configuration.

Deployment Spec

A deployment spec is used to represent the desired state of your deployment. With the Gradient CLI you can use a YAML file to change the desired state of your deployment.

Deployment Run

A deployment can have multiple runs at the same time. Any update to the deployment spec can create a new deployment run. Once the latest deployment run is ready, the previous deployment run will scale down.
Last modified 2mo ago
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Key concepts