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| 1 | +// Copyright 2023 Google LLC |
| 2 | +// |
| 3 | +// Licensed under the Apache License, Version 2.0 (the "License"); |
| 4 | +// you may not use this file except in compliance with the License. |
| 5 | +// You may obtain a copy of the License at |
| 6 | +// |
| 7 | +// http://www.apache.org/licenses/LICENSE-2.0 |
| 8 | +// |
| 9 | +// Unless required by applicable law or agreed to in writing, software |
| 10 | +// distributed under the License is distributed on an "AS IS" BASIS, |
| 11 | +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| 12 | +// See the License for the specific language governing permissions and |
| 13 | +// limitations under the License. |
| 14 | + |
| 15 | +syntax = "proto3"; |
| 16 | + |
| 17 | +package google.cloud.aiplatform.v1beta1; |
| 18 | + |
| 19 | +import "google/api/field_behavior.proto"; |
| 20 | +import "google/api/resource.proto"; |
| 21 | +import "google/cloud/aiplatform/v1beta1/explanation.proto"; |
| 22 | +import "google/cloud/aiplatform/v1beta1/model_monitoring_spec.proto"; |
| 23 | +import "google/protobuf/timestamp.proto"; |
| 24 | + |
| 25 | +option csharp_namespace = "Google.Cloud.AIPlatform.V1Beta1"; |
| 26 | +option go_package = "cloud.google.com/go/aiplatform/apiv1beta1/aiplatformpb;aiplatformpb"; |
| 27 | +option java_multiple_files = true; |
| 28 | +option java_outer_classname = "ModelMonitorProto"; |
| 29 | +option java_package = "com.google.cloud.aiplatform.v1beta1"; |
| 30 | +option php_namespace = "Google\\Cloud\\AIPlatform\\V1beta1"; |
| 31 | +option ruby_package = "Google::Cloud::AIPlatform::V1beta1"; |
| 32 | + |
| 33 | +// Vertex AI Model Monitoring Service serves as a central hub for the analysis |
| 34 | +// and visualization of data quality and performance related to models. |
| 35 | +// ModelMonitor stands as a top level resource for overseeing your model |
| 36 | +// monitoring tasks. |
| 37 | +message ModelMonitor { |
| 38 | + option (google.api.resource) = { |
| 39 | + type: "aiplatform.googleapis.com/ModelMonitor" |
| 40 | + pattern: "projects/{project}/locations/{location}/modelMonitors/{model_monitor}" |
| 41 | + }; |
| 42 | + |
| 43 | + // The monitoring target refers to the entity that is subject to analysis. |
| 44 | + // e.g. Vertex AI Model version. |
| 45 | + message ModelMonitoringTarget { |
| 46 | + // Model in Vertex AI Model Registry. |
| 47 | + message VertexModelSource { |
| 48 | + // Model resource name. Format: |
| 49 | + // projects/{project}/locations/{location}/models/{model}. |
| 50 | + string model = 1 [(google.api.resource_reference) = { |
| 51 | + type: "aiplatform.googleapis.com/Model" |
| 52 | + }]; |
| 53 | + |
| 54 | + // Model version id. |
| 55 | + string model_version_id = 2; |
| 56 | + } |
| 57 | + |
| 58 | + oneof source { |
| 59 | + // Model in Vertex AI Model Registry. |
| 60 | + VertexModelSource vertex_model = 1; |
| 61 | + } |
| 62 | + } |
| 63 | + |
| 64 | + // Optional default monitoring objective, it can be overridden in the |
| 65 | + // ModelMonitoringJob objective spec. |
| 66 | + oneof default_objective { |
| 67 | + // Optional default tabular model monitoring objective. |
| 68 | + ModelMonitoringObjectiveSpec.TabularObjective tabular_objective = 11; |
| 69 | + } |
| 70 | + |
| 71 | + // Immutable. Resource name of the ModelMonitor. Format: |
| 72 | + // `projects/{project}/locations/{location}/modelMonitors/{model_monitor}`. |
| 73 | + string name = 1 [(google.api.field_behavior) = IMMUTABLE]; |
| 74 | + |
| 75 | + // The display name of the ModelMonitor. |
| 76 | + // The name can be up to 128 characters long and can consist of any UTF-8. |
| 77 | + string display_name = 2; |
| 78 | + |
| 79 | + // The entity that is subject to analysis. |
| 80 | + // Currently only models in Vertex AI Model Registry are supported. If you |
| 81 | + // want to analyze the model which is outside the Vertex AI, you could |
| 82 | + // register a model in Vertex AI Model Registry using just a display name. |
| 83 | + ModelMonitoringTarget model_monitoring_target = 3; |
| 84 | + |
| 85 | + // Optional training dataset used to train the model. |
| 86 | + // It can serve as a reference dataset to identify changes in production. |
| 87 | + ModelMonitoringInput training_dataset = 10; |
| 88 | + |
| 89 | + // Optional default notification spec, it can be overridden in the |
| 90 | + // ModelMonitoringJob notification spec. |
| 91 | + ModelMonitoringNotificationSpec notification_spec = 12; |
| 92 | + |
| 93 | + // Optional default monitoring metrics/logs export spec, it can be overridden |
| 94 | + // in the ModelMonitoringJob output spec. |
| 95 | + // If not specified, a default Google Cloud Storage bucket will be created |
| 96 | + // under your project. |
| 97 | + ModelMonitoringOutputSpec output_spec = 13; |
| 98 | + |
| 99 | + // Optional model explanation spec. It is used for feature attribution |
| 100 | + // monitoring. |
| 101 | + ExplanationSpec explanation_spec = 16; |
| 102 | + |
| 103 | + // Monitoring Schema is to specify the model's features, prediction outputs |
| 104 | + // and ground truth properties. It is used to extract pertinent data from the |
| 105 | + // dataset and to process features based on their properties. |
| 106 | + // Make sure that the schema aligns with your dataset, if it does not, we will |
| 107 | + // be unable to extract data from the dataset. |
| 108 | + // It is required for most models, but optional for Vertex AI AutoML Tables |
| 109 | + // unless the schem information is not available. |
| 110 | + ModelMonitoringSchema model_monitoring_schema = 9; |
| 111 | + |
| 112 | + // Output only. Timestamp when this ModelMonitor was created. |
| 113 | + google.protobuf.Timestamp create_time = 6 |
| 114 | + [(google.api.field_behavior) = OUTPUT_ONLY]; |
| 115 | + |
| 116 | + // Output only. Timestamp when this ModelMonitor was updated most recently. |
| 117 | + google.protobuf.Timestamp update_time = 7 |
| 118 | + [(google.api.field_behavior) = OUTPUT_ONLY]; |
| 119 | +} |
| 120 | + |
| 121 | +// The Model Monitoring Schema definition. |
| 122 | +message ModelMonitoringSchema { |
| 123 | + // Schema field definition. |
| 124 | + message FieldSchema { |
| 125 | + // Field name. |
| 126 | + string name = 1; |
| 127 | + |
| 128 | + // Supported data types are: |
| 129 | + // `float` |
| 130 | + // `integer` |
| 131 | + // `boolean` |
| 132 | + // `string` |
| 133 | + // `categorical` |
| 134 | + string data_type = 2; |
| 135 | + |
| 136 | + // Describes if the schema field is an array of given data type. |
| 137 | + bool repeated = 3; |
| 138 | + } |
| 139 | + |
| 140 | + // Feature names of the model. Vertex AI will try to match the features from |
| 141 | + // your dataset as follows: |
| 142 | + // * For 'csv' files, the header names are required, and we will extract the |
| 143 | + // corresponding feature values when the header names align with the |
| 144 | + // feature names. |
| 145 | + // * For 'jsonl' files, we will extract the corresponding feature values if |
| 146 | + // the key names match the feature names. |
| 147 | + // Note: Nested features are not supported, so please ensure your features |
| 148 | + // are flattened. Ensure the feature values are scalar or an array of |
| 149 | + // scalars. |
| 150 | + // * For 'bigquery' dataset, we will extract the corresponding feature values |
| 151 | + // if the column names match the feature names. |
| 152 | + // Note: The column type can be a scalar or an array of scalars. STRUCT or |
| 153 | + // JSON types are not supported. You may use SQL queries to select or |
| 154 | + // aggregate the relevant features from your original table. However, |
| 155 | + // ensure that the 'schema' of the query results meets our requirements. |
| 156 | + // * For the Vertex AI Endpoint Request Response Logging table or Vertex AI |
| 157 | + // Batch Prediction Job results. If the |
| 158 | + // [instance_type][google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.instance_type] |
| 159 | + // is an array, ensure that the sequence in |
| 160 | + // [feature_fields][google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.feature_fields] |
| 161 | + // matches the order of features in the prediction instance. We will match |
| 162 | + // the feature with the array in the order specified in [feature_fields]. |
| 163 | + repeated FieldSchema feature_fields = 1; |
| 164 | + |
| 165 | + // Prediction output names of the model. The requirements are the same as the |
| 166 | + // [feature_fields][google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.feature_fields]. |
| 167 | + // For AutoML Tables, the prediction output name presented in schema will be: |
| 168 | + // `predicted_{target_column}`, the `target_column` is the one you specified |
| 169 | + // when you train the model. |
| 170 | + // For Prediction output drift analysis: |
| 171 | + // * AutoML Classification, the distribution of the argmax label will be |
| 172 | + // analyzed. |
| 173 | + // * AutoML Regression, the distribution of the value will be analyzed. |
| 174 | + repeated FieldSchema prediction_fields = 2; |
| 175 | + |
| 176 | + // Target /ground truth names of the model. |
| 177 | + repeated FieldSchema ground_truth_fields = 3; |
| 178 | + |
| 179 | + // The prediction instance type that the Model accepts when serving. |
| 180 | + // Supported values are: |
| 181 | + // * `object`: Each input is a JSON object format. |
| 182 | + // * `array`: Each input is a JSON array format. |
| 183 | + string instance_type = 4; |
| 184 | +} |
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