
When this happens...
New Deploy Started
Deploy Succeeded after failing
New Deploy Failed
New Form Submission
Deploy Created

Automatically do this!
List Training Projects
Create Secret
List All Secrets
List All Teams
Create Training Project
When this happensTriggers
A trigger is an event that starts a workflow.
Triggers when a new deploy of your site has started building.
Triggers when a new version of your site has successfully deployed after failing.
Triggers when a new deploy of your site has failed.
Triggers when a form receives a new submission.
Triggers when a new deploy is created.
Action is the task that follows automatically within your Netlify integrations.
Performs a new deploy of an existing site.
List all training projects
Store a named secret value.
Retrieve all Baseten secrets
Get all teams in your account
Create a training project

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To start, connect both your Netlify and Baseten accounts to viaSocket. Once connected, you can set up a workflow where an event in Netlify triggers actions in Baseten (or vice versa).
Absolutely. You can customize how Netlify data is recorded in Baseten. This includes choosing which data fields go into which fields of Baseten, setting up custom formats, and filtering out unwanted information.
The data sync between Netlify and Baseten typically happens in real-time through instant triggers. And a maximum of 15 minutes in case of a scheduled trigger.
Yes, viaSocket allows you to add custom logic or use built-in filters to modify data according to your needs.
Yes, you can set conditional logic to control the flow of data between Netlify and Baseten. For instance, you can specify that data should only be sent if certain conditions are met, or you can create if/else statements to manage different outcomes.
Netlify is a platform that automates the deployment and management of modern web projects. It provides continuous deployment, a powerful build environment, and a global CDN to deliver your sites quickly and securely.
Learn MoreBaseten is a platform that empowers developers to build, deploy, and manage machine learning models with ease. It provides tools for integrating AI capabilities into applications, streamlining the process from model creation to deployment.
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