bigframes.ml.remote.VertexAIModel#
- class bigframes.ml.remote.VertexAIModel(endpoint: str, input: Mapping[str, str], output: Mapping[str, str], *, session: Session | None = None, connection_name: str | None = None)[source]#
Remote model from a Vertex AI HTTPS endpoint. User must specify HTTPS endpoint, input schema and output schema. For more information, see Deploy model on Vertex AI: https://cloud.google.com/bigquery/docs/bigquery-ml-remote-model-tutorial#Deploy-Model-on-Vertex-AI.
- Parameters:
endpoint (str) – Vertex AI HTTPS endpoint.
input (Mapping) – Input schema: {column_name: column_type}. Supported types are “bool”, “string”, “int64”, “float64”, “array<bool>”, “array<string>”, “array<int64>”, “array<float64>”.
output (Mapping) – Output label schema: {column_name: column_type}. Supported the same types as the input.
session (bigframes.Session or None) – BQ session to create the model. If None, use the global default session.
connection_name (str or None) – Connection to connect with remote service. str of the format <PROJECT_NUMBER/PROJECT_ID>.<LOCATION>.<CONNECTION_ID>. If None, use default connection in session context. BigQuery DataFrame will try to create the connection and attach permission if the connection isn’t fully set up.
Methods
__init__(endpoint, input, output, *[, ...])get_params([deep])Get parameters for this estimator.
predict(X)Predict the result from the input DataFrame.