Try Gemini 1.5 models, our newest multimodal models in Vertex AI, and see what you can build with a 1M token context window. Try Gemini 1.5 models, our newest multimodal models in Vertex AI, and see what you can build with a 1M token context window.

ModelMonitoringInput

Model monitoring data input spec.

JSON representation

JSON representation
{ // Union field `dataset` can be only one of the following: "columnizedDataset": { object (`ModelMonitoringDataset`) }, "batchPredictionOutput": { object (`BatchPredictionOutput`) }, "vertexEndpointLogs": { object (`VertexEndpointLogs`) } // End of list of possible types for union field `dataset`. // Union field `time_spec` can be only one of the following: "timeInterval": { object (`Interval`) }, "timeOffset": { object (`TimeOffset`) } // End of list of possible types for union field `time_spec`. }

{

  // Union field dataset can be only one of the following:
  "columnizedDataset": {
    object (ModelMonitoringDataset)
  },
  "batchPredictionOutput": {
    object (BatchPredictionOutput)
  },
  "vertexEndpointLogs": {
    object (VertexEndpointLogs)
  }
  // End of list of possible types for union field dataset.

  // Union field time_spec can be only one of the following:
  "timeInterval": {
    object (Interval)
  },
  "timeOffset": {
    object (TimeOffset)
  }
  // End of list of possible types for union field time_spec.
}

Fields
Union field `dataset`. Dataset source. `dataset` can be only one of the following:
`columnizedDataset`	`object (ModelMonitoringDataset)` Columnized dataset.
`batchPredictionOutput`	`object (BatchPredictionOutput)` Vertex AI Batch prediction Job.
`vertexEndpointLogs`	`object (VertexEndpointLogs)` Vertex AI Endpoint request & response logging.
Union field `time_spec`. Time specification for the dataset. `time_spec` can be only one of the following:
`timeInterval`	`object (Interval)` The time interval (pair of startTime and endTime) for which results should be returned.
`timeOffset`	`object (TimeOffset)` The time offset setting for which results should be returned.

ModelMonitoringDataset

Input dataset spec.

JSON representation

JSON representation
{ "timestampField": string, // Union field `data_location` can be only one of the following: "vertexDataset": string, "gcsSource": { object (`ModelMonitoringGcsSource`) }, "bigquerySource": { object (`ModelMonitoringBigQuerySource`) } // End of list of possible types for union field `data_location`. }

{
  "timestampField": string,

  // Union field data_location can be only one of the following:
  "vertexDataset": string,
  "gcsSource": {
    object (ModelMonitoringGcsSource)
  },
  "bigquerySource": {
    object (ModelMonitoringBigQuerySource)
  }
  // End of list of possible types for union field data_location.
}

Fields
`timestampField`	`string` The timestamp field. Usually for serving data.
Union field `data_location`. Choose one of supported data location for columnized dataset. `data_location` can be only one of the following:
`vertexDataset`	`string` Resource name of the Vertex AI managed dataset.
`gcsSource`	`object (ModelMonitoringGcsSource)` Google Cloud Storage data source.
`bigquerySource`	`object (ModelMonitoringBigQuerySource)` BigQuery data source.

ModelMonitoringGcsSource

Dataset spec for data stored in Google Cloud Storage.

JSON representation
{ "gcsUri": string, "format": enum (`DataFormat`) }

Fields

Fields
`gcsUri`	`string` Google Cloud Storage URI to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.
`format`	`enum (DataFormat)` Data format of the dataset.

gcsUri

string

Google Cloud Storage URI to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.

format

enum (DataFormat)

Data format of the dataset.

DataFormat

Supported data format.

Enums
`DATA_FORMAT_UNSPECIFIED`	Data format unspecified, used when this field is unset.
`CSV`	CSV files.
`TF_RECORD`	TfRecord files
`JSONL`	JsonL files.

ModelMonitoringBigQuerySource

Dataset spec for data sotred in BigQuery.

JSON representation
{ // Union field `connection` can be only one of the following: "tableUri": string, "query": string // End of list of possible types for union field `connection`. }

Fields

Fields
Union field `connection`. `connection` can be only one of the following:
`tableUri`	`string` BigQuery URI to a table, up to 2000 characters long. All the columns in the table will be selected. Accepted forms: BigQuery path. For example: `bq://projectId.bqDatasetId.bqTableId`.
`query`	`string` Standard SQL to be used instead of the `tableUri`.

Union field connection.

connection can be only one of the following:

tableUri

string

BigQuery URI to a table, up to 2000 characters long. All the columns in the table will be selected. Accepted forms:

BigQuery path. For example: bq://projectId.bqDatasetId.bqTableId.

query

string

Standard SQL to be used instead of the tableUri.

BatchPredictionOutput

Data from Vertex AI Batch prediction job output.

JSON representation
{ "batchPredictionJob": string }

Fields

Fields
`batchPredictionJob`	`string` Vertex AI Batch prediction job resource name. The job must match the model version specified in [ModelMonitor].[modelMonitoringTarget].

batchPredictionJob

string

Vertex AI Batch prediction job resource name. The job must match the model version specified in [ModelMonitor].[modelMonitoringTarget].

VertexEndpointLogs

Data from Vertex AI Endpoint request response logging.

JSON representation
{ "endpoints": [ string ] }

Fields

Fields
`endpoints[]`	`string` List of endpoint resource names. The endpoints must enable the logging with the [Endpoint].[requestResponseLoggingConfig], and must contain the deployed model corresponding to the model version specified in [ModelMonitor].[modelMonitoringTarget].

endpoints[]

string

List of endpoint resource names. The endpoints must enable the logging with the [Endpoint].[requestResponseLoggingConfig], and must contain the deployed model corresponding to the model version specified in [ModelMonitor].[modelMonitoringTarget].

TimeOffset

time offset setting.

JSON representation
{ "offset": string, "window": string }

Fields

Fields
`offset`	`string` [offset] is the time difference from the cut-off time. For scheduled jobs, the cut-off time is the scheduled time. For non-scheduled jobs, it's the time when the job was created. Currently we support the following format: 'w\|W': Week, 'd\|D': Day, 'h\|H': Hour E.g. '1h' stands for 1 hour, '2d' stands for 2 days.
`window`	`string` [window] refers to the scope of data selected for analysis. It allows you to specify the quantity of data you wish to examine. Currently we support the following format: 'w\|W': Week, 'd\|D': Day, 'h\|H': Hour E.g. '1h' stands for 1 hour, '2d' stands for 2 days.

offset

string

[offset] is the time difference from the cut-off time. For scheduled jobs, the cut-off time is the scheduled time. For non-scheduled jobs, it's the time when the job was created. Currently we support the following format: 'w|W': Week, 'd|D': Day, 'h|H': Hour E.g. '1h' stands for 1 hour, '2d' stands for 2 days.

window

string

[window] refers to the scope of data selected for analysis. It allows you to specify the quantity of data you wish to examine. Currently we support the following format: 'w|W': Week, 'd|D': Day, 'h|H': Hour E.g. '1h' stands for 1 hour, '2d' stands for 2 days.