Workflows.

BETA

Workflows

Workflows provides a simple way to automate machine learning tasks.

Supercharge your workflow with a CI/CD approach for machine learning. Install Gradient on any repo and train models directly from pull requests or commits. Build reproducible, maintainable, and deterministic models without ever configuring servers.

Read the docs

Run a workflow when you push code.

Link your source code with Gradient and trigger training from Git commits.

01

Connect

Install the GitHub app on your repo and connect to a Gradient project.

02

Train

Every time you push code a Gradient Workflow will be triggered.

03

Repeat

Iterate quickly and in parallel. Create continuously updated ML models.

Connect your account

Install the app and create a Gradient project.

Linking your Git repo takes just a few seconds. Once connected, your ML training will be tightly coupled with your source code.

Define your pipeline

Add a simple .yaml file to define your pipeline steps.

See sample projects on GitHub

Push to train

Make changes to your code, then push. Our built-in CI/CD system triggers on every code change.

▲ ~ fashion-app/ git push

Version your models

Use the Create Model Gradient Action to automatically capture and save models to the model repository.

The Gradient model repository is a hub for importing, managing, and deploying ML models.

Deploy as an API endpoint

🚀 Ship

Serve your trained model 🎉

Gradient makes model inference simple and scalable. Deploy any model as a high-performance, low-latency micro-service with a RESTful API. Easily monitor, scale, and version deployments.

"Our partnership with Paperspace will boost our system’s advanced analytics so that we can better enable cities to remotely and continuously control their wastewater quality. Accordingly, we will begin to see greater wastewater reuse, cleaner environments, and healthier communities."

Ari Goldfarb, Kando CEO

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