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feat: add model_monitor resource and APIs to public v1beta1 client li…
…brary PiperOrigin-RevId: 624288685
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| // Copyright 2023 Google LLC | ||
| // | ||
| // Licensed under the Apache License, Version 2.0 (the "License"); | ||
| // you may not use this file except in compliance with the License. | ||
| // You may obtain a copy of the License at | ||
| // | ||
| // http://www.apache.org/licenses/LICENSE-2.0 | ||
| // | ||
| // Unless required by applicable law or agreed to in writing, software | ||
| // distributed under the License is distributed on an "AS IS" BASIS, | ||
| // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| // See the License for the specific language governing permissions and | ||
| // limitations under the License. | ||
|
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| syntax = "proto3"; | ||
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| package google.cloud.aiplatform.v1beta1; | ||
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| import "google/api/field_behavior.proto"; | ||
| import "google/api/resource.proto"; | ||
| import "google/cloud/aiplatform/v1beta1/explanation.proto"; | ||
| import "google/cloud/aiplatform/v1beta1/model_monitoring_spec.proto"; | ||
| import "google/protobuf/timestamp.proto"; | ||
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| option csharp_namespace = "Google.Cloud.AIPlatform.V1Beta1"; | ||
| option go_package = "cloud.google.com/go/aiplatform/apiv1beta1/aiplatformpb;aiplatformpb"; | ||
| option java_multiple_files = true; | ||
| option java_outer_classname = "ModelMonitorProto"; | ||
| option java_package = "com.google.cloud.aiplatform.v1beta1"; | ||
| option php_namespace = "Google\\Cloud\\AIPlatform\\V1beta1"; | ||
| option ruby_package = "Google::Cloud::AIPlatform::V1beta1"; | ||
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| // Vertex AI Model Monitoring Service serves as a central hub for the analysis | ||
| // and visualization of data quality and performance related to models. | ||
| // ModelMonitor stands as a top level resource for overseeing your model | ||
| // monitoring tasks. | ||
| message ModelMonitor { | ||
| option (google.api.resource) = { | ||
| type: "aiplatform.googleapis.com/ModelMonitor" | ||
| pattern: "projects/{project}/locations/{location}/modelMonitors/{model_monitor}" | ||
| }; | ||
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| // The monitoring target refers to the entity that is subject to analysis. | ||
| // e.g. Vertex AI Model version. | ||
| message ModelMonitoringTarget { | ||
| // Model in Vertex AI Model Registry. | ||
| message VertexModelSource { | ||
| // Model resource name. Format: | ||
| // projects/{project}/locations/{location}/models/{model}. | ||
| string model = 1 [(google.api.resource_reference) = { | ||
| type: "aiplatform.googleapis.com/Model" | ||
| }]; | ||
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| // Model version id. | ||
| string model_version_id = 2; | ||
| } | ||
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| oneof source { | ||
| // Model in Vertex AI Model Registry. | ||
| VertexModelSource vertex_model = 1; | ||
| } | ||
| } | ||
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| // Optional default monitoring objective, it can be overridden in the | ||
| // ModelMonitoringJob objective spec. | ||
| oneof default_objective { | ||
| // Optional default tabular model monitoring objective. | ||
| ModelMonitoringObjectiveSpec.TabularObjective tabular_objective = 11; | ||
| } | ||
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| // Immutable. Resource name of the ModelMonitor. Format: | ||
| // `projects/{project}/locations/{location}/modelMonitors/{model_monitor}`. | ||
| string name = 1 [(google.api.field_behavior) = IMMUTABLE]; | ||
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| // The display name of the ModelMonitor. | ||
| // The name can be up to 128 characters long and can consist of any UTF-8. | ||
| string display_name = 2; | ||
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| // The entity that is subject to analysis. | ||
| // Currently only models in Vertex AI Model Registry are supported. If you | ||
| // want to analyze the model which is outside the Vertex AI, you could | ||
| // register a model in Vertex AI Model Registry using just a display name. | ||
| ModelMonitoringTarget model_monitoring_target = 3; | ||
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| // Optional training dataset used to train the model. | ||
| // It can serve as a reference dataset to identify changes in production. | ||
| ModelMonitoringInput training_dataset = 10; | ||
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| // Optional default notification spec, it can be overridden in the | ||
| // ModelMonitoringJob notification spec. | ||
| ModelMonitoringNotificationSpec notification_spec = 12; | ||
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| // Optional default monitoring metrics/logs export spec, it can be overridden | ||
| // in the ModelMonitoringJob output spec. | ||
| // If not specified, a default Google Cloud Storage bucket will be created | ||
| // under your project. | ||
| ModelMonitoringOutputSpec output_spec = 13; | ||
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| // Optional model explanation spec. It is used for feature attribution | ||
| // monitoring. | ||
| ExplanationSpec explanation_spec = 16; | ||
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| // Monitoring Schema is to specify the model's features, prediction outputs | ||
| // and ground truth properties. It is used to extract pertinent data from the | ||
| // dataset and to process features based on their properties. | ||
| // Make sure that the schema aligns with your dataset, if it does not, we will | ||
| // be unable to extract data from the dataset. | ||
| // It is required for most models, but optional for Vertex AI AutoML Tables | ||
| // unless the schem information is not available. | ||
| ModelMonitoringSchema model_monitoring_schema = 9; | ||
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| // Output only. Timestamp when this ModelMonitor was created. | ||
| google.protobuf.Timestamp create_time = 6 | ||
| [(google.api.field_behavior) = OUTPUT_ONLY]; | ||
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| // Output only. Timestamp when this ModelMonitor was updated most recently. | ||
| google.protobuf.Timestamp update_time = 7 | ||
| [(google.api.field_behavior) = OUTPUT_ONLY]; | ||
| } | ||
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| // The Model Monitoring Schema definition. | ||
| message ModelMonitoringSchema { | ||
| // Schema field definition. | ||
| message FieldSchema { | ||
| // Field name. | ||
| string name = 1; | ||
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| // Supported data types are: | ||
| // `float` | ||
| // `integer` | ||
| // `boolean` | ||
| // `string` | ||
| // `categorical` | ||
| string data_type = 2; | ||
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| // Describes if the schema field is an array of given data type. | ||
| bool repeated = 3; | ||
| } | ||
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| // Feature names of the model. Vertex AI will try to match the features from | ||
| // your dataset as follows: | ||
| // * For 'csv' files, the header names are required, and we will extract the | ||
| // corresponding feature values when the header names align with the | ||
| // feature names. | ||
| // * For 'jsonl' files, we will extract the corresponding feature values if | ||
| // the key names match the feature names. | ||
| // Note: Nested features are not supported, so please ensure your features | ||
| // are flattened. Ensure the feature values are scalar or an array of | ||
| // scalars. | ||
| // * For 'bigquery' dataset, we will extract the corresponding feature values | ||
| // if the column names match the feature names. | ||
| // Note: The column type can be a scalar or an array of scalars. STRUCT or | ||
| // JSON types are not supported. You may use SQL queries to select or | ||
| // aggregate the relevant features from your original table. However, | ||
| // ensure that the 'schema' of the query results meets our requirements. | ||
| // * For the Vertex AI Endpoint Request Response Logging table or Vertex AI | ||
| // Batch Prediction Job results. If the | ||
| // [instance_type][google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.instance_type] | ||
| // is an array, ensure that the sequence in | ||
| // [feature_fields][google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.feature_fields] | ||
| // matches the order of features in the prediction instance. We will match | ||
| // the feature with the array in the order specified in [feature_fields]. | ||
| repeated FieldSchema feature_fields = 1; | ||
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| // Prediction output names of the model. The requirements are the same as the | ||
| // [feature_fields][google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.feature_fields]. | ||
| // For AutoML Tables, the prediction output name presented in schema will be: | ||
| // `predicted_{target_column}`, the `target_column` is the one you specified | ||
| // when you train the model. | ||
| // For Prediction output drift analysis: | ||
| // * AutoML Classification, the distribution of the argmax label will be | ||
| // analyzed. | ||
| // * AutoML Regression, the distribution of the value will be analyzed. | ||
| repeated FieldSchema prediction_fields = 2; | ||
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| // Target /ground truth names of the model. | ||
| repeated FieldSchema ground_truth_fields = 3; | ||
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| // The prediction instance type that the Model accepts when serving. | ||
| // Supported values are: | ||
| // * `object`: Each input is a JSON object format. | ||
| // * `array`: Each input is a JSON array format. | ||
| string instance_type = 4; | ||
| } |
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google/cloud/aiplatform/v1beta1/model_monitoring_alert.proto
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| // Copyright 2023 Google LLC | ||
| // | ||
| // Licensed under the Apache License, Version 2.0 (the "License"); | ||
| // you may not use this file except in compliance with the License. | ||
| // You may obtain a copy of the License at | ||
| // | ||
| // http://www.apache.org/licenses/LICENSE-2.0 | ||
| // | ||
| // Unless required by applicable law or agreed to in writing, software | ||
| // distributed under the License is distributed on an "AS IS" BASIS, | ||
| // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| // See the License for the specific language governing permissions and | ||
| // limitations under the License. | ||
|
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| syntax = "proto3"; | ||
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| package google.cloud.aiplatform.v1beta1; | ||
|
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| import "google/protobuf/struct.proto"; | ||
| import "google/protobuf/timestamp.proto"; | ||
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| option csharp_namespace = "Google.Cloud.AIPlatform.V1Beta1"; | ||
| option go_package = "cloud.google.com/go/aiplatform/apiv1beta1/aiplatformpb;aiplatformpb"; | ||
| option java_multiple_files = true; | ||
| option java_outer_classname = "ModelMonitoringAlertProto"; | ||
| option java_package = "com.google.cloud.aiplatform.v1beta1"; | ||
| option php_namespace = "Google\\Cloud\\AIPlatform\\V1beta1"; | ||
| option ruby_package = "Google::Cloud::AIPlatform::V1beta1"; | ||
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| // Monitoring alert triggered condition. | ||
| message ModelMonitoringAlertCondition { | ||
| // Alert triggered condition. | ||
| oneof condition { | ||
| // A condition that compares a stats value against a threshold. Alert will | ||
| // be triggered if value above the threshold. | ||
| double threshold = 1; | ||
| } | ||
| } | ||
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| // Represents a single model monitoring anomaly. | ||
| message ModelMonitoringAnomaly { | ||
| // Tabular anomaly details. | ||
| message TabularAnomaly { | ||
| // Additional anomaly information. e.g. Google Cloud Storage uri. | ||
| string anomaly_uri = 1; | ||
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| // Overview of this anomaly. | ||
| string summary = 2; | ||
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| // Anomaly body. | ||
| google.protobuf.Value anomaly = 3; | ||
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| // The time the anomaly was triggered. | ||
| google.protobuf.Timestamp trigger_time = 4; | ||
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| // The alert condition associated with this anomaly. | ||
| ModelMonitoringAlertCondition condition = 5; | ||
| } | ||
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| oneof anomaly { | ||
| // Tabular anomaly. | ||
| TabularAnomaly tabular_anomaly = 1; | ||
| } | ||
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| // Model monitoring job resource name. | ||
| string model_monitoring_job = 2; | ||
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| // Algorithm used to calculated the metrics, eg: jensen_shannon_divergence, | ||
| // l_infinity. | ||
| string algorithm = 3; | ||
| } | ||
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| // Represents a single monitoring alert. This is currently used in the | ||
| // SearchModelMonitoringAlerts api, thus the alert wrapped in this message | ||
| // belongs to the resource asked in the request. | ||
| message ModelMonitoringAlert { | ||
| // The stats name. | ||
| string stats_name = 1; | ||
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| // One of the supported monitoring objectives: | ||
| // `raw-feature-drift` | ||
| // `prediction-output-drift` | ||
| // `feature-attribution` | ||
| string objective_type = 2; | ||
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| // Alert creation time. | ||
| google.protobuf.Timestamp alert_time = 3; | ||
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| // Anomaly details. | ||
| ModelMonitoringAnomaly anomaly = 4; | ||
| } |
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