About

Gradient Deployments enable a hassle-free, automatic “push to deploy” option for any trained model. These allow ML practitioners to quickly validate “end-to-end” services from R&D to production.

Note: Gradient Deployments are still in Beta. During the Beta period we do not recommend running mission-critical services. Additionally, API endpoints could be renamed/updated and there are certain restrictions placed on accounts. Learn more about restrictions below

Overview

Deploy any model as a high-performance, low-latency micro-service with a RESTful API. Easily monitor, scale, and version deployments. Deployments take a trained model and expose them as a persistent service at a known URI.

Current Limitations

During the Beta period, Gradient Deployments have a number of restrictions.

  • Teams are limited to a single running deployment.

  • Only K80 and CPU nodes are supported.

  • The exposed endpoint URI is subject to change.

  • Billing for deployments is not currently enabled. You will not see Gradient Deployments on your invoice.

Deployment States

Deployments go through a series of states. They are enumerated here:

ID

Name

1

Building

2

Provisioning

3

Starting

4

Running

5

Stopping

6

Stopped

7

Error