---
title: Getting Started with Datadog
description: Datadog, the leading service for cloud-scale monitoring.
breadcrumbs: Docs > Infrastructure > Datadog Resource Catalog
---

# gcp_aiplatform_model{% #gcp_aiplatform_model %}

## `ancestors`{% #ancestors %}

**Type**: `UNORDERED_LIST_STRING`

## `artifact_uri`{% #artifact_uri %}

**Type**: `STRING`**Provider name**: `artifactUri`**Description**: Immutable. The path to the directory containing the Model artifact and any of its supporting files. Not required for AutoML Models.

## `base_model_source`{% #base_model_source %}

**Type**: `STRUCT`**Provider name**: `baseModelSource`**Description**: Optional. User input field to specify the base model source. Currently it only supports specifing the Model Garden models and Genie models.

- `genie_source`**Type**: `STRUCT`**Provider name**: `genieSource`**Description**: Information about the base model of Genie models.
  - `base_model_uri`**Type**: `STRING`**Provider name**: `baseModelUri`**Description**: Required. The public base model URI.
- `model_garden_source`**Type**: `STRUCT`**Provider name**: `modelGardenSource`**Description**: Source information of Model Garden models.
  - `public_model_name`**Type**: `STRING`**Provider name**: `publicModelName`**Description**: Required. The model garden source model resource name.
  - `skip_hf_model_cache`**Type**: `BOOLEAN`**Provider name**: `skipHfModelCache`**Description**: Optional. Whether to avoid pulling the model from the HF cache.
  - `version_id`**Type**: `STRING`**Provider name**: `versionId`**Description**: Optional. The model garden source model version ID.

## `container_spec`{% #container_spec %}

**Type**: `STRUCT`**Provider name**: `containerSpec`**Description**: Input only. The specification of the container that is to be used when deploying this Model. The specification is ingested upon ModelService.UploadModel, and all binaries it contains are copied and stored internally by Vertex AI. Not required for AutoML Models.

- `args`**Type**: `UNORDERED_LIST_STRING`**Provider name**: `args`**Description**: Immutable. Specifies arguments for the command that runs when the container starts. This overrides the container's [`CMD`](https://docs.docker.com/engine/reference/builder/#cmd). Specify this field as an array of executable and arguments, similar to a Docker `CMD`'s "default parameters" form. If you don't specify this field but do specify the command field, then the command from the `command` field runs without any additional arguments. See the [Kubernetes documentation about how the `command` and `args` fields interact with a container's `ENTRYPOINT` and `CMD`](https://kubernetes.io/docs/tasks/inject-data-application/define-command-argument-container/#notes). If you don't specify this field and don't specify the `command` field, then the container's [`ENTRYPOINT`](https://docs.docker.com/engine/reference/builder/#cmd) and `CMD` determine what runs based on their default behavior. See the Docker documentation about [how `CMD` and `ENTRYPOINT` interact](https://docs.docker.com/engine/reference/builder/#understand-how-cmd-and-entrypoint-interact). In this field, you can reference [environment variables set by Vertex AI](https://cloud.google.com/vertex-ai/docs/predictions/custom-container-requirements#aip-variables) and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with `$$`; for example: $$(VARIABLE_NAME) This field corresponds to the `args` field of the Kubernetes Containers [v1 core API](https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.23/#container-v1-core).
- `command`**Type**: `UNORDERED_LIST_STRING`**Provider name**: `command`**Description**: Immutable. Specifies the command that runs when the container starts. This overrides the container's [ENTRYPOINT](https://docs.docker.com/engine/reference/builder/#entrypoint). Specify this field as an array of executable and arguments, similar to a Docker `ENTRYPOINT`'s "exec" form, not its "shell" form. If you do not specify this field, then the container's `ENTRYPOINT` runs, in conjunction with the args field or the container's [`CMD`](https://docs.docker.com/engine/reference/builder/#cmd), if either exists. If this field is not specified and the container does not have an `ENTRYPOINT`, then refer to the Docker documentation about [how `CMD` and `ENTRYPOINT` interact](https://docs.docker.com/engine/reference/builder/#understand-how-cmd-and-entrypoint-interact). If you specify this field, then you can also specify the `args` field to provide additional arguments for this command. However, if you specify this field, then the container's `CMD` is ignored. See the [Kubernetes documentation about how the `command` and `args` fields interact with a container's `ENTRYPOINT` and `CMD`](https://kubernetes.io/docs/tasks/inject-data-application/define-command-argument-container/#notes). In this field, you can reference [environment variables set by Vertex AI](https://cloud.google.com/vertex-ai/docs/predictions/custom-container-requirements#aip-variables) and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with `$$`; for example: $$(VARIABLE_NAME) This field corresponds to the `command` field of the Kubernetes Containers [v1 core API](https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.23/#container-v1-core).
- `deployment_timeout`**Type**: `STRING`**Provider name**: `deploymentTimeout`**Description**: Immutable. Deployment timeout. Limit for deployment timeout is 2 hours.
- `env`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `env`**Description**: Immutable. List of environment variables to set in the container. After the container starts running, code running in the container can read these environment variables. Additionally, the command and args fields can reference these variables. Later entries in this list can also reference earlier entries. For example, the following example sets the variable `VAR_2` to have the value `foo bar`: `json [ { "name": "VAR_1", "value": "foo" }, { "name": "VAR_2", "value": "$(VAR_1) bar" } ]`If you switch the order of the variables in the example, then the expansion does not occur. This field corresponds to the `env` field of the Kubernetes Containers [v1 core API](https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.23/#container-v1-core).
  - `name`**Type**: `STRING`**Provider name**: `name`**Description**: Required. Name of the environment variable. Must be a valid C identifier.
  - `value`**Type**: `STRING`**Provider name**: `value`**Description**: Required. Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
- `grpc_ports`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `grpcPorts`**Description**: Immutable. List of ports to expose from the container. Vertex AI sends gRPC prediction requests that it receives to the first port on this list. Vertex AI also sends liveness and health checks to this port. If you do not specify this field, gRPC requests to the container will be disabled. Vertex AI does not use ports other than the first one listed. This field corresponds to the `ports` field of the Kubernetes Containers v1 core API.
  - `container_port`**Type**: `INT32`**Provider name**: `containerPort`**Description**: The number of the port to expose on the pod's IP address. Must be a valid port number, between 1 and 65535 inclusive.
- `health_probe`**Type**: `STRUCT`**Provider name**: `healthProbe`**Description**: Immutable. Specification for Kubernetes readiness probe.
  - `exec`**Type**: `STRUCT`**Provider name**: `exec`**Description**: ExecAction probes the health of a container by executing a command.
    - `command`**Type**: `UNORDERED_LIST_STRING`**Provider name**: `command`**Description**: Command is the command line to execute inside the container, the working directory for the command is root ('/') in the container's filesystem. The command is simply exec'd, it is not run inside a shell, so traditional shell instructions ('|', etc) won't work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy.
  - `failure_threshold`**Type**: `INT32`**Provider name**: `failureThreshold`**Description**: Number of consecutive failures before the probe is considered failed. Defaults to 3. Minimum value is 1. Maps to Kubernetes probe argument 'failureThreshold'.
  - `grpc`**Type**: `STRUCT`**Provider name**: `grpc`**Description**: GrpcAction probes the health of a container by sending a gRPC request.
    - `port`**Type**: `INT32`**Provider name**: `port`**Description**: Port number of the gRPC service. Number must be in the range 1 to 65535.
    - `service`**Type**: `STRING`**Provider name**: `service`**Description**: Service is the name of the service to place in the gRPC HealthCheckRequest. See [https://github.com/grpc/grpc/blob/master/doc/health-checking.md](https://github.com/grpc/grpc/blob/master/doc/health-checking.md). If this is not specified, the default behavior is defined by gRPC.
  - `http_get`**Type**: `STRUCT`**Provider name**: `httpGet`**Description**: HttpGetAction probes the health of a container by sending an HTTP GET request.
    - `host`**Type**: `STRING`**Provider name**: `host`**Description**: Host name to connect to, defaults to the model serving container's IP. You probably want to set "Host" in httpHeaders instead.
    - `http_headers`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `httpHeaders`**Description**: Custom headers to set in the request. HTTP allows repeated headers.
      - `name`**Type**: `STRING`**Provider name**: `name`**Description**: The header field name. This will be canonicalized upon output, so case-variant names will be understood as the same header.
      - `value`**Type**: `STRING`**Provider name**: `value`**Description**: The header field value
    - `path`**Type**: `STRING`**Provider name**: `path`**Description**: Path to access on the HTTP server.
    - `port`**Type**: `INT32`**Provider name**: `port`**Description**: Number of the port to access on the container. Number must be in the range 1 to 65535.
    - `scheme`**Type**: `STRING`**Provider name**: `scheme`**Description**: Scheme to use for connecting to the host. Defaults to HTTP. Acceptable values are "HTTP" or "HTTPS".
  - `initial_delay_seconds`**Type**: `INT32`**Provider name**: `initialDelaySeconds`**Description**: Number of seconds to wait before starting the probe. Defaults to 0. Minimum value is 0. Maps to Kubernetes probe argument 'initialDelaySeconds'.
  - `period_seconds`**Type**: `INT32`**Provider name**: `periodSeconds`**Description**: How often (in seconds) to perform the probe. Default to 10 seconds. Minimum value is 1. Must be less than timeout_seconds. Maps to Kubernetes probe argument 'periodSeconds'.
  - `success_threshold`**Type**: `INT32`**Provider name**: `successThreshold`**Description**: Number of consecutive successes before the probe is considered successful. Defaults to 1. Minimum value is 1. Maps to Kubernetes probe argument 'successThreshold'.
  - `tcp_socket`**Type**: `STRUCT`**Provider name**: `tcpSocket`**Description**: TcpSocketAction probes the health of a container by opening a TCP socket connection.
    - `host`**Type**: `STRING`**Provider name**: `host`**Description**: Optional: Host name to connect to, defaults to the model serving container's IP.
    - `port`**Type**: `INT32`**Provider name**: `port`**Description**: Number of the port to access on the container. Number must be in the range 1 to 65535.
  - `timeout_seconds`**Type**: `INT32`**Provider name**: `timeoutSeconds`**Description**: Number of seconds after which the probe times out. Defaults to 1 second. Minimum value is 1. Must be greater or equal to period_seconds. Maps to Kubernetes probe argument 'timeoutSeconds'.
- `health_route`**Type**: `STRING`**Provider name**: `healthRoute`**Description**: Immutable. HTTP path on the container to send health checks to. Vertex AI intermittently sends GET requests to this path on the container's IP address and port to check that the container is healthy. Read more about [health checks](https://cloud.google.com/vertex-ai/docs/predictions/custom-container-requirements#health). For example, if you set this field to `/bar`, then Vertex AI intermittently sends a GET request to the `/bar` path on the port of your container specified by the first value of this `ModelContainerSpec`'s ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/ DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following `endpoints/`)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the [`AIP_ENDPOINT_ID` environment variable](https://cloud.google.com/vertex-ai/docs/predictions/custom-container-requirements#aip-variables).) * DEPLOYED_MODEL: DeployedModel.id of the `DeployedModel`. (Vertex AI makes this value available to your container code as the [`AIP_DEPLOYED_MODEL_ID` environment variable](https://cloud.google.com/vertex-ai/docs/predictions/custom-container-requirements#aip-variables).)
- `image_uri`**Type**: `STRING`**Provider name**: `imageUri`**Description**: Required. Immutable. URI of the Docker image to be used as the custom container for serving predictions. This URI must identify an image in Artifact Registry or Container Registry. Learn more about the [container publishing requirements](https://cloud.google.com/vertex-ai/docs/predictions/custom-container-requirements#publishing), including permissions requirements for the Vertex AI Service Agent. The container image is ingested upon ModelService.UploadModel, stored internally, and this original path is afterwards not used. To learn about the requirements for the Docker image itself, see [Custom container requirements](https://cloud.google.com/vertex-ai/docs/predictions/custom-container-requirements#). You can use the URI to one of Vertex AI's [pre-built container images for prediction](https://cloud.google.com/vertex-ai/docs/predictions/pre-built-containers) in this field.
- `liveness_probe`**Type**: `STRUCT`**Provider name**: `livenessProbe`**Description**: Immutable. Specification for Kubernetes liveness probe.
  - `exec`**Type**: `STRUCT`**Provider name**: `exec`**Description**: ExecAction probes the health of a container by executing a command.
    - `command`**Type**: `UNORDERED_LIST_STRING`**Provider name**: `command`**Description**: Command is the command line to execute inside the container, the working directory for the command is root ('/') in the container's filesystem. The command is simply exec'd, it is not run inside a shell, so traditional shell instructions ('|', etc) won't work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy.
  - `failure_threshold`**Type**: `INT32`**Provider name**: `failureThreshold`**Description**: Number of consecutive failures before the probe is considered failed. Defaults to 3. Minimum value is 1. Maps to Kubernetes probe argument 'failureThreshold'.
  - `grpc`**Type**: `STRUCT`**Provider name**: `grpc`**Description**: GrpcAction probes the health of a container by sending a gRPC request.
    - `port`**Type**: `INT32`**Provider name**: `port`**Description**: Port number of the gRPC service. Number must be in the range 1 to 65535.
    - `service`**Type**: `STRING`**Provider name**: `service`**Description**: Service is the name of the service to place in the gRPC HealthCheckRequest. See [https://github.com/grpc/grpc/blob/master/doc/health-checking.md](https://github.com/grpc/grpc/blob/master/doc/health-checking.md). If this is not specified, the default behavior is defined by gRPC.
  - `http_get`**Type**: `STRUCT`**Provider name**: `httpGet`**Description**: HttpGetAction probes the health of a container by sending an HTTP GET request.
    - `host`**Type**: `STRING`**Provider name**: `host`**Description**: Host name to connect to, defaults to the model serving container's IP. You probably want to set "Host" in httpHeaders instead.
    - `http_headers`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `httpHeaders`**Description**: Custom headers to set in the request. HTTP allows repeated headers.
      - `name`**Type**: `STRING`**Provider name**: `name`**Description**: The header field name. This will be canonicalized upon output, so case-variant names will be understood as the same header.
      - `value`**Type**: `STRING`**Provider name**: `value`**Description**: The header field value
    - `path`**Type**: `STRING`**Provider name**: `path`**Description**: Path to access on the HTTP server.
    - `port`**Type**: `INT32`**Provider name**: `port`**Description**: Number of the port to access on the container. Number must be in the range 1 to 65535.
    - `scheme`**Type**: `STRING`**Provider name**: `scheme`**Description**: Scheme to use for connecting to the host. Defaults to HTTP. Acceptable values are "HTTP" or "HTTPS".
  - `initial_delay_seconds`**Type**: `INT32`**Provider name**: `initialDelaySeconds`**Description**: Number of seconds to wait before starting the probe. Defaults to 0. Minimum value is 0. Maps to Kubernetes probe argument 'initialDelaySeconds'.
  - `period_seconds`**Type**: `INT32`**Provider name**: `periodSeconds`**Description**: How often (in seconds) to perform the probe. Default to 10 seconds. Minimum value is 1. Must be less than timeout_seconds. Maps to Kubernetes probe argument 'periodSeconds'.
  - `success_threshold`**Type**: `INT32`**Provider name**: `successThreshold`**Description**: Number of consecutive successes before the probe is considered successful. Defaults to 1. Minimum value is 1. Maps to Kubernetes probe argument 'successThreshold'.
  - `tcp_socket`**Type**: `STRUCT`**Provider name**: `tcpSocket`**Description**: TcpSocketAction probes the health of a container by opening a TCP socket connection.
    - `host`**Type**: `STRING`**Provider name**: `host`**Description**: Optional: Host name to connect to, defaults to the model serving container's IP.
    - `port`**Type**: `INT32`**Provider name**: `port`**Description**: Number of the port to access on the container. Number must be in the range 1 to 65535.
  - `timeout_seconds`**Type**: `INT32`**Provider name**: `timeoutSeconds`**Description**: Number of seconds after which the probe times out. Defaults to 1 second. Minimum value is 1. Must be greater or equal to period_seconds. Maps to Kubernetes probe argument 'timeoutSeconds'.
- `ports`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `ports`**Description**: Immutable. List of ports to expose from the container. Vertex AI sends any prediction requests that it receives to the first port on this list. Vertex AI also sends [liveness and health checks](https://cloud.google.com/vertex-ai/docs/predictions/custom-container-requirements#liveness) to this port. If you do not specify this field, it defaults to following value: `json [ { "containerPort": 8080 } ]`Vertex AI does not use ports other than the first one listed. This field corresponds to the `ports` field of the Kubernetes Containers [v1 core API](https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.23/#container-v1-core).
  - `container_port`**Type**: `INT32`**Provider name**: `containerPort`**Description**: The number of the port to expose on the pod's IP address. Must be a valid port number, between 1 and 65535 inclusive.
- `predict_route`**Type**: `STRING`**Provider name**: `predictRoute`**Description**: Immutable. HTTP path on the container to send prediction requests to. Vertex AI forwards requests sent using projects.locations.endpoints.predict to this path on the container's IP address and port. Vertex AI then returns the container's response in the API response. For example, if you set this field to `/foo`, then when Vertex AI receives a prediction request, it forwards the request body in a POST request to the `/foo` path on the port of your container specified by the first value of this `ModelContainerSpec`'s ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following `endpoints/`)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the [`AIP_ENDPOINT_ID` environment variable](https://cloud.google.com/vertex-ai/docs/predictions/custom-container-requirements#aip-variables).) * DEPLOYED_MODEL: DeployedModel.id of the `DeployedModel`. (Vertex AI makes this value available to your container code as the [`AIP_DEPLOYED_MODEL_ID` environment variable](https://cloud.google.com/vertex-ai/docs/predictions/custom-container-requirements#aip-variables).)
- `shared_memory_size_mb`**Type**: `INT64`**Provider name**: `sharedMemorySizeMb`**Description**: Immutable. The amount of the VM memory to reserve as the shared memory for the model in megabytes.
- `startup_probe`**Type**: `STRUCT`**Provider name**: `startupProbe`**Description**: Immutable. Specification for Kubernetes startup probe.
  - `exec`**Type**: `STRUCT`**Provider name**: `exec`**Description**: ExecAction probes the health of a container by executing a command.
    - `command`**Type**: `UNORDERED_LIST_STRING`**Provider name**: `command`**Description**: Command is the command line to execute inside the container, the working directory for the command is root ('/') in the container's filesystem. The command is simply exec'd, it is not run inside a shell, so traditional shell instructions ('|', etc) won't work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy.
  - `failure_threshold`**Type**: `INT32`**Provider name**: `failureThreshold`**Description**: Number of consecutive failures before the probe is considered failed. Defaults to 3. Minimum value is 1. Maps to Kubernetes probe argument 'failureThreshold'.
  - `grpc`**Type**: `STRUCT`**Provider name**: `grpc`**Description**: GrpcAction probes the health of a container by sending a gRPC request.
    - `port`**Type**: `INT32`**Provider name**: `port`**Description**: Port number of the gRPC service. Number must be in the range 1 to 65535.
    - `service`**Type**: `STRING`**Provider name**: `service`**Description**: Service is the name of the service to place in the gRPC HealthCheckRequest. See [https://github.com/grpc/grpc/blob/master/doc/health-checking.md](https://github.com/grpc/grpc/blob/master/doc/health-checking.md). If this is not specified, the default behavior is defined by gRPC.
  - `http_get`**Type**: `STRUCT`**Provider name**: `httpGet`**Description**: HttpGetAction probes the health of a container by sending an HTTP GET request.
    - `host`**Type**: `STRING`**Provider name**: `host`**Description**: Host name to connect to, defaults to the model serving container's IP. You probably want to set "Host" in httpHeaders instead.
    - `http_headers`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `httpHeaders`**Description**: Custom headers to set in the request. HTTP allows repeated headers.
      - `name`**Type**: `STRING`**Provider name**: `name`**Description**: The header field name. This will be canonicalized upon output, so case-variant names will be understood as the same header.
      - `value`**Type**: `STRING`**Provider name**: `value`**Description**: The header field value
    - `path`**Type**: `STRING`**Provider name**: `path`**Description**: Path to access on the HTTP server.
    - `port`**Type**: `INT32`**Provider name**: `port`**Description**: Number of the port to access on the container. Number must be in the range 1 to 65535.
    - `scheme`**Type**: `STRING`**Provider name**: `scheme`**Description**: Scheme to use for connecting to the host. Defaults to HTTP. Acceptable values are "HTTP" or "HTTPS".
  - `initial_delay_seconds`**Type**: `INT32`**Provider name**: `initialDelaySeconds`**Description**: Number of seconds to wait before starting the probe. Defaults to 0. Minimum value is 0. Maps to Kubernetes probe argument 'initialDelaySeconds'.
  - `period_seconds`**Type**: `INT32`**Provider name**: `periodSeconds`**Description**: How often (in seconds) to perform the probe. Default to 10 seconds. Minimum value is 1. Must be less than timeout_seconds. Maps to Kubernetes probe argument 'periodSeconds'.
  - `success_threshold`**Type**: `INT32`**Provider name**: `successThreshold`**Description**: Number of consecutive successes before the probe is considered successful. Defaults to 1. Minimum value is 1. Maps to Kubernetes probe argument 'successThreshold'.
  - `tcp_socket`**Type**: `STRUCT`**Provider name**: `tcpSocket`**Description**: TcpSocketAction probes the health of a container by opening a TCP socket connection.
    - `host`**Type**: `STRING`**Provider name**: `host`**Description**: Optional: Host name to connect to, defaults to the model serving container's IP.
    - `port`**Type**: `INT32`**Provider name**: `port`**Description**: Number of the port to access on the container. Number must be in the range 1 to 65535.
  - `timeout_seconds`**Type**: `INT32`**Provider name**: `timeoutSeconds`**Description**: Number of seconds after which the probe times out. Defaults to 1 second. Minimum value is 1. Must be greater or equal to period_seconds. Maps to Kubernetes probe argument 'timeoutSeconds'.

## `create_time`{% #create_time %}

**Type**: `TIMESTAMP`**Provider name**: `createTime`**Description**: Output only. Timestamp when this Model was uploaded into Vertex AI.

## `data_stats`{% #data_stats %}

**Type**: `STRUCT`**Provider name**: `dataStats`**Description**: Stats of data used for training or evaluating the Model. Only populated when the Model is trained by a TrainingPipeline with data_input_config.

- `test_annotations_count`**Type**: `INT64`**Provider name**: `testAnnotationsCount`**Description**: Number of Annotations that are used for evaluating this Model. If the Model is evaluated multiple times, this will be the number of test Annotations used by the first evaluation. If the Model is not evaluated, the number is 0.
- `test_data_items_count`**Type**: `INT64`**Provider name**: `testDataItemsCount`**Description**: Number of DataItems that were used for evaluating this Model. If the Model is evaluated multiple times, this will be the number of test DataItems used by the first evaluation. If the Model is not evaluated, the number is 0.
- `training_annotations_count`**Type**: `INT64`**Provider name**: `trainingAnnotationsCount`**Description**: Number of Annotations that are used for training this Model.
- `training_data_items_count`**Type**: `INT64`**Provider name**: `trainingDataItemsCount`**Description**: Number of DataItems that were used for training this Model.
- `validation_annotations_count`**Type**: `INT64`**Provider name**: `validationAnnotationsCount`**Description**: Number of Annotations that are used for validating this Model during training.
- `validation_data_items_count`**Type**: `INT64`**Provider name**: `validationDataItemsCount`**Description**: Number of DataItems that were used for validating this Model during training.

## `default_checkpoint_id`{% #default_checkpoint_id %}

**Type**: `STRING`**Provider name**: `defaultCheckpointId`**Description**: The default checkpoint id of a model version.

## `deployed_models`{% #deployed_models %}

**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `deployedModels`**Description**: Output only. The pointers to DeployedModels created from this Model. Note that Model could have been deployed to Endpoints in different Locations.

- `deployed_model_id`**Type**: `STRING`**Provider name**: `deployedModelId`**Description**: Immutable. An ID of a DeployedModel in the above Endpoint.
- `endpoint`**Type**: `STRING`**Provider name**: `endpoint`**Description**: Immutable. A resource name of an Endpoint.

## `description`{% #description %}

**Type**: `STRING`**Provider name**: `description`**Description**: The description of the Model.

## `encryption_spec`{% #encryption_spec %}

**Type**: `STRUCT`**Provider name**: `encryptionSpec`**Description**: Customer-managed encryption key spec for a Model. If set, this Model and all sub-resources of this Model will be secured by this key.

- `kms_key_name`**Type**: `STRING`**Provider name**: `kmsKeyName`**Description**: Required. The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: `projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key`. The key needs to be in the same region as where the compute resource is created.

## `etag`{% #etag %}

**Type**: `STRING`**Provider name**: `etag`**Description**: Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.

## `explanation_spec`{% #explanation_spec %}

**Type**: `STRUCT`**Provider name**: `explanationSpec`**Description**: The default explanation specification for this Model. The Model can be used for requesting explanation after being deployed if it is populated. The Model can be used for batch explanation if it is populated. All fields of the explanation_spec can be overridden by explanation_spec of DeployModelRequest.deployed_model, or explanation_spec of BatchPredictionJob. If the default explanation specification is not set for this Model, this Model can still be used for requesting explanation by setting explanation_spec of DeployModelRequest.deployed_model and for batch explanation by setting explanation_spec of BatchPredictionJob.

- `metadata`**Type**: `STRUCT`**Provider name**: `metadata`**Description**: Optional. Metadata describing the Model's input and output for explanation.
  - `feature_attributions_schema_uri`**Type**: `STRING`**Provider name**: `featureAttributionsSchemaUri`**Description**: Points to a YAML file stored on Google Cloud Storage describing the format of the feature attributions. The schema is defined as an OpenAPI 3.0.2 [Schema Object](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject). AutoML tabular Models always have this field populated by Vertex AI. Note: The URI given on output may be different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
  - `latent_space_source`**Type**: `STRING`**Provider name**: `latentSpaceSource`**Description**: Name of the source to generate embeddings for example based explanations.
- `parameters`**Type**: `STRUCT`**Provider name**: `parameters`**Description**: Required. Parameters that configure explaining of the Model's predictions.
  - `examples`**Type**: `STRUCT`**Provider name**: `examples`**Description**: Example-based explanations that returns the nearest neighbors from the provided dataset.
    - `example_gcs_source`**Type**: `STRUCT`**Provider name**: `exampleGcsSource`**Description**: The Cloud Storage input instances.
      - `data_format`**Type**: `STRING`**Provider name**: `dataFormat`**Description**: The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.**Possible values**:
        - `DATA_FORMAT_UNSPECIFIED` - Format unspecified, used when unset.
        - `JSONL` - Examples are stored in JSONL files.
      - `gcs_source`**Type**: `STRUCT`**Provider name**: `gcsSource`**Description**: The Cloud Storage location for the input instances.
        - `uris`**Type**: `UNORDERED_LIST_STRING`**Provider name**: `uris`**Description**: Required. Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see [https://cloud.google.com/storage/docs/wildcards](https://cloud.google.com/storage/docs/wildcards).
    - `neighbor_count`**Type**: `INT32`**Provider name**: `neighborCount`**Description**: The number of neighbors to return when querying for examples.
    - `presets`**Type**: `STRUCT`**Provider name**: `presets`**Description**: Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.
      - `modality`**Type**: `STRING`**Provider name**: `modality`**Description**: The modality of the uploaded model, which automatically configures the distance measurement and feature normalization for the underlying example index and queries. If your model does not precisely fit one of these types, it is okay to choose the closest type.**Possible values**:
        - `MODALITY_UNSPECIFIED` - Should not be set. Added as a recommended best practice for enums
        - `IMAGE` - IMAGE modality
        - `TEXT` - TEXT modality
        - `TABULAR` - TABULAR modality
      - `query`**Type**: `STRING`**Provider name**: `query`**Description**: Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to `PRECISE`.**Possible values**:
        - `PRECISE` - More precise neighbors as a trade-off against slower response.
        - `FAST` - Faster response as a trade-off against less precise neighbors.
  - `integrated_gradients_attribution`**Type**: `STRUCT`**Provider name**: `integratedGradientsAttribution`**Description**: An attribution method that computes Aumann-Shapley values taking advantage of the model's fully differentiable structure. Refer to this paper for more details: [https://arxiv.org/abs/1703.01365](https://arxiv.org/abs/1703.01365)
    - `blur_baseline_config`**Type**: `STRUCT`**Provider name**: `blurBaselineConfig`**Description**: Config for IG with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: [https://arxiv.org/abs/2004.03383](https://arxiv.org/abs/2004.03383)
      - `max_blur_sigma`**Type**: `FLOAT`**Provider name**: `maxBlurSigma`**Description**: The standard deviation of the blur kernel for the blurred baseline. The same blurring parameter is used for both the height and the width dimension. If not set, the method defaults to the zero (i.e. black for images) baseline.
    - `smooth_grad_config`**Type**: `STRUCT`**Provider name**: `smoothGradConfig`**Description**: Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: [https://arxiv.org/pdf/1706.03825.pdf](https://arxiv.org/pdf/1706.03825.pdf)
      - `feature_noise_sigma`**Type**: `STRUCT`**Provider name**: `featureNoiseSigma`**Description**: This is similar to noise_sigma, but provides additional flexibility. A separate noise sigma can be provided for each feature, which is useful if their distributions are different. No noise is added to features that are not set. If this field is unset, noise_sigma will be used for all features.
        - `noise_sigma`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `noiseSigma`**Description**: Noise sigma per feature. No noise is added to features that are not set.
          - `name`**Type**: `STRING`**Provider name**: `name`**Description**: The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
          - `sigma`**Type**: `FLOAT`**Provider name**: `sigma`**Description**: This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.
      - `noise_sigma`**Type**: `FLOAT`**Provider name**: `noiseSigma`**Description**: This is a single float value and will be used to add noise to all the features. Use this field when all features are normalized to have the same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where features are normalized to have 0-mean and 1-variance. Learn more about [normalization](https://developers.google.com/machine-learning/data-prep/transform/normalization). For best results the recommended value is about 10% - 20% of the standard deviation of the input feature. Refer to section 3.2 of the SmoothGrad paper: [https://arxiv.org/pdf/1706.03825.pdf](https://arxiv.org/pdf/1706.03825.pdf). Defaults to 0.1. If the distribution is different per feature, set feature_noise_sigma instead for each feature.
      - `noisy_sample_count`**Type**: `INT32`**Provider name**: `noisySampleCount`**Description**: The number of gradient samples to use for approximation. The higher this number, the more accurate the gradient is, but the runtime complexity increases by this factor as well. Valid range of its value is [1, 50]. Defaults to 3.
    - `step_count`**Type**: `INT32`**Provider name**: `stepCount`**Description**: Required. The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is within the desired error range. Valid range of its value is [1, 100], inclusively.
  - `sampled_shapley_attribution`**Type**: `STRUCT`**Provider name**: `sampledShapleyAttribution`**Description**: An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features. Refer to this paper for model details: [https://arxiv.org/abs/1306.4265](https://arxiv.org/abs/1306.4265).
    - `path_count`**Type**: `INT32`**Provider name**: `pathCount`**Description**: Required. The number of feature permutations to consider when approximating the Shapley values. Valid range of its value is [1, 50], inclusively.
  - `top_k`**Type**: `INT32`**Provider name**: `topK`**Description**: If populated, returns attributions for top K indices of outputs (defaults to 1). Only applies to Models that predicts more than one outputs (e,g, multi-class Models). When set to -1, returns explanations for all outputs.
  - `xrai_attribution`**Type**: `STRUCT`**Provider name**: `xraiAttribution`**Description**: An attribution method that redistributes Integrated Gradients attribution to segmented regions, taking advantage of the model's fully differentiable structure. Refer to this paper for more details: [https://arxiv.org/abs/1906.02825](https://arxiv.org/abs/1906.02825) XRAI currently performs better on natural images, like a picture of a house or an animal. If the images are taken in artificial environments, like a lab or manufacturing line, or from diagnostic equipment, like x-rays or quality-control cameras, use Integrated Gradients instead.
    - `blur_baseline_config`**Type**: `STRUCT`**Provider name**: `blurBaselineConfig`**Description**: Config for XRAI with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: [https://arxiv.org/abs/2004.03383](https://arxiv.org/abs/2004.03383)
      - `max_blur_sigma`**Type**: `FLOAT`**Provider name**: `maxBlurSigma`**Description**: The standard deviation of the blur kernel for the blurred baseline. The same blurring parameter is used for both the height and the width dimension. If not set, the method defaults to the zero (i.e. black for images) baseline.
    - `smooth_grad_config`**Type**: `STRUCT`**Provider name**: `smoothGradConfig`**Description**: Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: [https://arxiv.org/pdf/1706.03825.pdf](https://arxiv.org/pdf/1706.03825.pdf)
      - `feature_noise_sigma`**Type**: `STRUCT`**Provider name**: `featureNoiseSigma`**Description**: This is similar to noise_sigma, but provides additional flexibility. A separate noise sigma can be provided for each feature, which is useful if their distributions are different. No noise is added to features that are not set. If this field is unset, noise_sigma will be used for all features.
        - `noise_sigma`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `noiseSigma`**Description**: Noise sigma per feature. No noise is added to features that are not set.
          - `name`**Type**: `STRING`**Provider name**: `name`**Description**: The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
          - `sigma`**Type**: `FLOAT`**Provider name**: `sigma`**Description**: This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.
      - `noise_sigma`**Type**: `FLOAT`**Provider name**: `noiseSigma`**Description**: This is a single float value and will be used to add noise to all the features. Use this field when all features are normalized to have the same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where features are normalized to have 0-mean and 1-variance. Learn more about [normalization](https://developers.google.com/machine-learning/data-prep/transform/normalization). For best results the recommended value is about 10% - 20% of the standard deviation of the input feature. Refer to section 3.2 of the SmoothGrad paper: [https://arxiv.org/pdf/1706.03825.pdf](https://arxiv.org/pdf/1706.03825.pdf). Defaults to 0.1. If the distribution is different per feature, set feature_noise_sigma instead for each feature.
      - `noisy_sample_count`**Type**: `INT32`**Provider name**: `noisySampleCount`**Description**: The number of gradient samples to use for approximation. The higher this number, the more accurate the gradient is, but the runtime complexity increases by this factor as well. Valid range of its value is [1, 50]. Defaults to 3.
    - `step_count`**Type**: `INT32`**Provider name**: `stepCount`**Description**: Required. The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range. Valid range of its value is [1, 100], inclusively.

## `gcp_display_name`{% #gcp_display_name %}

**Type**: `STRING`**Provider name**: `displayName`**Description**: Required. The display name of the Model. The name can be up to 128 characters long and can consist of any UTF-8 characters.

## `labels`{% #labels %}

**Type**: `UNORDERED_LIST_STRING`

## `metadata_artifact`{% #metadata_artifact %}

**Type**: `STRING`**Provider name**: `metadataArtifact`**Description**: Output only. The resource name of the Artifact that was created in MetadataStore when creating the Model. The Artifact resource name pattern is `projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}`.

## `metadata_schema_uri`{% #metadata_schema_uri %}

**Type**: `STRING`**Provider name**: `metadataSchemaUri`**Description**: Immutable. Points to a YAML file stored on Google Cloud Storage describing additional information about the Model, that is specific to it. Unset if the Model does not have any additional information. The schema is defined as an OpenAPI 3.0.2 [Schema Object](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject). AutoML Models always have this field populated by Vertex AI, if no additional metadata is needed, this field is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.

## `model_source_info`{% #model_source_info %}

**Type**: `STRUCT`**Provider name**: `modelSourceInfo`**Description**: Output only. Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or saved and tuned from Genie or Model Garden.

- `copy`**Type**: `BOOLEAN`**Provider name**: `copy`**Description**: If this Model is copy of another Model. If true then source_type pertains to the original.
- `source_type`**Type**: `STRING`**Provider name**: `sourceType`**Description**: Type of the model source.**Possible values**:
  - `MODEL_SOURCE_TYPE_UNSPECIFIED` - Should not be used.
  - `AUTOML` - The Model is uploaded by automl training pipeline.
  - `CUSTOM` - The Model is uploaded by user or custom training pipeline.
  - `BQML` - The Model is registered and sync'ed from BigQuery ML.
  - `MODEL_GARDEN` - The Model is saved or tuned from Model Garden.
  - `GENIE` - The Model is saved or tuned from Genie.
  - `CUSTOM_TEXT_EMBEDDING` - The Model is uploaded by text embedding finetuning pipeline.
  - `MARKETPLACE` - The Model is saved or tuned from Marketplace.

## `name`{% #name %}

**Type**: `STRING`**Provider name**: `name`**Description**: The resource name of the Model.

## `organization_id`{% #organization_id %}

**Type**: `STRING`

## `original_model_info`{% #original_model_info %}

**Type**: `STRUCT`**Provider name**: `originalModelInfo`**Description**: Output only. If this Model is a copy of another Model, this contains info about the original.

- `model`**Type**: `STRING`**Provider name**: `model`**Description**: Output only. The resource name of the Model this Model is a copy of, including the revision. Format: `projects/{project}/locations/{location}/models/{model_id}@{version_id}`

## `parent`{% #parent %}

**Type**: `STRING`

## `pipeline_job`{% #pipeline_job %}

**Type**: `STRING`**Provider name**: `pipelineJob`**Description**: Optional. This field is populated if the model is produced by a pipeline job.

## `predict_schemata`{% #predict_schemata %}

**Type**: `STRUCT`**Provider name**: `predictSchemata`**Description**: The schemata that describe formats of the Model's predictions and explanations as given and returned via PredictionService.Predict and PredictionService.Explain.

- `instance_schema_uri`**Type**: `STRING`**Provider name**: `instanceSchemaUri`**Description**: Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single instance, which are used in PredictRequest.instances, ExplainRequest.instances and BatchPredictionJob.input_config. The schema is defined as an OpenAPI 3.0.2 [Schema Object](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject). AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
- `parameters_schema_uri`**Type**: `STRING`**Provider name**: `parametersSchemaUri`**Description**: Immutable. Points to a YAML file stored on Google Cloud Storage describing the parameters of prediction and explanation via PredictRequest.parameters, ExplainRequest.parameters and BatchPredictionJob.model_parameters. The schema is defined as an OpenAPI 3.0.2 [Schema Object](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject). AutoML Models always have this field populated by Vertex AI, if no parameters are supported, then it is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
- `prediction_schema_uri`**Type**: `STRING`**Provider name**: `predictionSchemaUri`**Description**: Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single prediction produced by this Model, which are returned via PredictResponse.predictions, ExplainResponse.explanations, and BatchPredictionJob.output_config. The schema is defined as an OpenAPI 3.0.2 [Schema Object](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject). AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.

## `project_id`{% #project_id %}

**Type**: `STRING`

## `project_number`{% #project_number %}

**Type**: `STRING`

## `region_id`{% #region_id %}

**Type**: `STRING`

## `resource_name`{% #resource_name %}

**Type**: `STRING`

## `satisfies_pzi`{% #satisfies_pzi %}

**Type**: `BOOLEAN`**Provider name**: `satisfiesPzi`**Description**: Output only. Reserved for future use.

## `satisfies_pzs`{% #satisfies_pzs %}

**Type**: `BOOLEAN`**Provider name**: `satisfiesPzs`**Description**: Output only. Reserved for future use.

## `supported_deployment_resources_types`{% #supported_deployment_resources_types %}

**Type**: `UNORDERED_LIST_STRING`**Provider name**: `supportedDeploymentResourcesTypes`**Description**: Output only. When this Model is deployed, its prediction resources are described by the `prediction_resources` field of the Endpoint.deployed_models object. Because not all Models support all resource configuration types, the configuration types this Model supports are listed here. If no configuration types are listed, the Model cannot be deployed to an Endpoint and does not support online predictions (PredictionService.Predict or PredictionService.Explain). Such a Model can serve predictions by using a BatchPredictionJob, if it has at least one entry each in supported_input_storage_formats and supported_output_storage_formats.

## `supported_export_formats`{% #supported_export_formats %}

**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `supportedExportFormats`**Description**: Output only. The formats in which this Model may be exported. If empty, this Model is not available for export.

- `exportable_contents`**Type**: `UNORDERED_LIST_STRING`**Provider name**: `exportableContents`**Description**: Output only. The content of this Model that may be exported.
- `id`**Type**: `STRING`**Provider name**: `id`**Description**: Output only. The ID of the export format. The possible format IDs are: * `tflite` Used for Android mobile devices. * `edgetpu-tflite` Used for [Edge TPU](https://cloud.google.com/edge-tpu/) devices. * `tf-saved-model` A tensorflow model in SavedModel format. * `tf-js` A [TensorFlow.js](https://www.tensorflow.org/js) model that can be used in the browser and in Node.js using JavaScript. * `core-ml` Used for iOS mobile devices. * `custom-trained` A Model that was uploaded or trained by custom code. * `genie` A tuned Model Garden model.

## `supported_input_storage_formats`{% #supported_input_storage_formats %}

**Type**: `UNORDERED_LIST_STRING`**Provider name**: `supportedInputStorageFormats`**Description**: Output only. The formats this Model supports in BatchPredictionJob.input_config. If PredictSchemata.instance_schema_uri exists, the instances should be given as per that schema. The possible formats are: * `jsonl` The JSON Lines format, where each instance is a single line. Uses GcsSource. * `csv` The CSV format, where each instance is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsSource. * `tf-record` The TFRecord format, where each instance is a single record in tfrecord syntax. Uses GcsSource. * `tf-record-gzip` Similar to `tf-record`, but the file is gzipped. Uses GcsSource. * `bigquery` Each instance is a single row in BigQuery. Uses BigQuerySource. * `file-list` Each line of the file is the location of an instance to process, uses `gcs_source` field of the InputConfig object. If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain.

## `supported_output_storage_formats`{% #supported_output_storage_formats %}

**Type**: `UNORDERED_LIST_STRING`**Provider name**: `supportedOutputStorageFormats`**Description**: Output only. The formats this Model supports in BatchPredictionJob.output_config. If both PredictSchemata.instance_schema_uri and PredictSchemata.prediction_schema_uri exist, the predictions are returned together with their instances. In other words, the prediction has the original instance data first, followed by the actual prediction content (as per the schema). The possible formats are: * `jsonl` The JSON Lines format, where each prediction is a single line. Uses GcsDestination. * `csv` The CSV format, where each prediction is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsDestination. * `bigquery` Each prediction is a single row in a BigQuery table, uses BigQueryDestination . If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain.

## `tags`{% #tags %}

**Type**: `UNORDERED_LIST_STRING`

## `training_pipeline`{% #training_pipeline %}

**Type**: `STRING`**Provider name**: `trainingPipeline`**Description**: Output only. The resource name of the TrainingPipeline that uploaded this Model, if any.

## `update_time`{% #update_time %}

**Type**: `TIMESTAMP`**Provider name**: `updateTime`**Description**: Output only. Timestamp when this Model was most recently updated.

## `version_aliases`{% #version_aliases %}

**Type**: `UNORDERED_LIST_STRING`**Provider name**: `versionAliases`**Description**: User provided version aliases so that a model version can be referenced via alias (i.e. `projects/{project}/locations/{location}/models/{model_id}@{version_alias}` instead of auto-generated version id (i.e. `projects/{project}/locations/{location}/models/{model_id}@{version_id})`. The format is a-z{0,126}[a-z0-9] to distinguish from version_id. A default version alias will be created for the first version of the model, and there must be exactly one default version alias for a model.

## `version_create_time`{% #version_create_time %}

**Type**: `TIMESTAMP`**Provider name**: `versionCreateTime`**Description**: Output only. Timestamp when this version was created.

## `version_description`{% #version_description %}

**Type**: `STRING`**Provider name**: `versionDescription`**Description**: The description of this version.

## `version_id`{% #version_id %}

**Type**: `STRING`**Provider name**: `versionId`**Description**: Output only. Immutable. The version ID of the model. A new version is committed when a new model version is uploaded or trained under an existing model id. It is an auto-incrementing decimal number in string representation.

## `version_update_time`{% #version_update_time %}

**Type**: `TIMESTAMP`**Provider name**: `versionUpdateTime`**Description**: Output only. Timestamp when this version was most recently updated.

## `zone_id`{% #zone_id %}

**Type**: `STRING`