How does OCNORA authenticate with Firebase ML?

With a stored OAuth 2.0 credential, shaped {"client_id", "client_secret", "refresh_token"}. A connection test can check it when you add it.

What can I automate with Firebase ML?

6 typed operations, each with a reference page listing its config fields and the shapes it emits.

What if the Firebase ML operation I need is not listed?

Where Firebase ML has an HTTP API, call it from the HTTP node with the same credential, or describe the step to the AI builder, which asks our team for a typed node when one is missing.

Are there templates that use Firebase ML?

Not yet. Start from a blank workflow, or describe the workflow to Build with AI.

How are Firebase ML calls controlled?

Every call and every run step is audited, runs count against the tenant’s token and USD budgets, and Workflow Health pre-flights the workflow when you save it.

Firebase ML

AI & modelsOAuth 2.0Connection test available

Access custom machine learning models hosted via Firebase ML (Google OAuth2).

OCNORA ships 6 typed Firebase ML operations. Each node has a schema-driven form, input and output tables beside its settings, and is pre-flighted by Workflow Health before your first real run.

Ask about Firebase ML Firebase ML API docs ↗

Before you connect Firebase ML

Authentication: OAuth 2.0
Credential: {"client_id", "client_secret", "refresh_token"}
Connection test: Available: the stored credential can be checked against the provider when you add it. This is not a record that a test was run or passed.

What you can do with Firebase ML

Operations, grouped by what they do to your data.

Create and send 1

Create Model

Read and search 3

Update 1

Update Model

Delete and cancel 1

Delete Model

A sample Firebase ML workflow

1
A core trigger
Start the run from a chat, form, manual, schedule, webhook, workflow trigger — no Firebase ML trigger is needed.
2
Download Modelfirebase_ml.download_model
Gets Download information for a model. This is meant for downloading model resources onto devices. It gives very limited information about the model.
3
Create Modelfirebase_ml.create_model
Creates a model in Firebase ML.

An outline built from this provider’s node contracts, not a recorded run. Each step’s output fields are available to the next in the field picker.

Every node below has a reference page: what it does, the connection it needs, its config fields with types, constraints and examples, and the shapes it emits.

Operations 6

Firebase ML questions

How does OCNORA authenticate with Firebase ML?: With a stored OAuth 2.0 credential, shaped {"client_id", "client_secret", "refresh_token"}. A connection test can check it when you add it.
What can I automate with Firebase ML?: 6 typed operations, each with a reference page listing its config fields and the shapes it emits.
What if the Firebase ML operation I need is not listed?: Where Firebase ML has an HTTP API, call it from the HTTP node with the same credential, or describe the step to the AI builder, which asks our team for a typed node when one is missing.
Are there templates that use Firebase ML?: Not yet. Start from a blank workflow, or describe the workflow to Build with AI.
How are Firebase ML calls controlled?: Every call and every run step is audited, runs count against the tenant’s token and USD budgets, and Workflow Health pre-flights the workflow when you save it.

See your first Firebase ML workflow run.

Book a technical demo. We add the connection, pick a template or describe the workflow to Build with AI, and Workflow Health checks it before it runs.

Book a technical demo Talk to sales