Announcement: On November 13, 2024, all users will need to use a Cloud project in order to access Earth Engine. After this date, continued individual access without a Cloud project will require an exception.

Predictions from Hosted Models

Earth Engine provides ee.Model as a connector to models hosted on Vertex AI. Earth Engine will send image or table data as online prediction requests to a trained model deployed on a Vertex AI endpoint. The model outputs are then available as Earth Engine images or tables.

TensorFlow Models

TensorFlow is an open source machine learning (ML) platform that supports advanced ML methods such as deep learning. The Earth Engine API provides methods for importing and or exporting imagery, training and testing data in TFRecord format. See the ML examples page for demonstrations that use TensorFlow with data from Earth Engine. See the TFRecord page for details about how Earth Engine writes data to TFRecord files.

`ee.Model`

The ee.Model package handles interaction with hosted machine learning models.

Hosted Models on Vertex AI

A new ee.Model instance can be created with ee.Model.fromVertexAi. This is an ee.Model object that packages Earth Engine data into tensors, forwards them as predict requests to Vertex AI then reassembles the responses into Earth Engine.

Earth Engine supports TensorFlow (e.g. a SavedModel format), PyTorch, and AutoML models. To prepare a model for hosting, save it, import it to Vertex AI, then deploy the model to an endpoint.

Input Formats

To interact with Earth Engine, a hosted model's inputs and outputs need to be compatible with a supported interchange format. The default is the TensorProto interchange format, specifically serialized TensorProtos in base64 (reference). This can be done programmatically, as shown on the ML examples page, after training and before saving, or by loading, adding the input and output transformation, and re-saving. Other supported payload formats include JSON with RAW_JSON and multi-dimensional arrays with ND_ARRAYS. See our payload format documentation for more details.

Endpoint IAM Permissions

To use a model with ee.Model.fromVertexAi(), you must have sufficient permissions to use the model. Specifically, you (or anyone who uses the model) needs at least the Vertex AI user role for the Cloud Project where the model is hosted. You control permissions for your Cloud Project using Identify and Access Management (IAM) controls.

Regions

When deploying your model to an endpoint, you will need to specify which region to deploy to. The us-central1 region is recommended since it will likely perform best due to proximity to Earth Engine servers, but almost any region will work. See the Vertex AI location docs for details about Vertex AI regions and what features each one supports.

If you are migrating from AI Platform, note that Vertex AI does not have a global endpoint, and ee.Model.fromVertexAi() does not have a region parameter.

Costs

For detailed information on costs, see each product's associated pricing page.

Vertex AI (pricing
Cloud Storage (pricing)
Earth Engine (pricing (commercial))

You can use the Pricing Calculator to generate a cost estimate based on your projected usage.