title | description |
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Steampipe Table: gcp_vertex_ai_model - Query GCP Vertex AI Models using SQL |
Allows users to query GCP Vertex AI Models, specifically the detailed information about each model in the Google Cloud project. |
Google Cloud's Vertex AI is a unified ML platform for building, deploying, and scaling AI applications. It offers a suite of tools and services for data scientists and ML engineers, which includes the ability to manage models. A model in Vertex AI is a resource that represents a machine learning solution, which can be trained and deployed to serve predictions.
The gcp_vertex_ai_model
table provides insights into Vertex AI models within Google Cloud Platform. As a data scientist or ML engineer, explore model-specific details through this table, including model type, training details, and associated metadata. Utilize it to manage and monitor your AI models, such as those serving high traffic, the training data used, and the status of each model.
Explore which AI models are active within your Google Cloud Platform, gaining insights into their creation time and associated networks. This can be particularly useful for managing and auditing your AI resources.
select
name,
display_name,
create_time,
version_id,
container_spec,
deployed_models
from
gcp_vertex_ai_model;
select
name,
display_name,
create_time,
version_id,
container_spec,
deployed_models
from
gcp_vertex_ai_model;
Determine the areas in which new models have been established within the past month. This can be useful for tracking recent changes and developments in your AI models.
select
name,
display_name,
create_time
from
gcp_vertex_ai_model
where
create_time >= current_date - interval '30 days';
select
name,
display_name,
create_time
from
gcp_vertex_ai_model
where
create_time >= date('now', '-30 days');
Determine the source of the model, whether it was imported from a TensorFlow model or a custom model, to understand the origin of the model and its compatibility with other tools and services.
select
name,
display_name,
case
when model_source_info ->> 'source_type' = '1' then 'Model Generated by AutoML'
when model_source_info ->> 'source_type' = '2' then 'Model Imported from Custom'
when model_source_info ->> 'source_type' = '3' then 'Model Imported from BigQuery ML'
when model_source_info ->> 'source_type' = '4' then 'Model Saved from Model Garden'
when model_source_info ->> 'source_type' = '5' then 'Model Saved from Genie'
end as model_source
from
gcp_vertex_ai_model;
select
name,
display_name,
case
when model_source_info ->> 'source_type' = '1' then 'Model Generated by AutoML'
when model_source_info ->> 'source_type' = '2' then 'Model Imported from Custom'
when model_source_info ->> 'source_type' = '3' then 'Model Imported from BigQuery ML'
when model_source_info ->> 'source_type' = '4' then 'Model Saved from Model Garden'
when model_source_info ->> 'source_type' = '5' then 'Model Saved from Genie'
end as model_source
from
gcp_vertex_ai_model;
Explore models that have been deployed to a specific endpoint, gaining insights into the models serving predictions for a particular application or service.
select
name,
display_name,
d ->> 'endpoint' as endpoint,
d ->> 'deployed_model_id' as deployed_model_id
from
gcp_vertex_ai_model,
jsonb_array_elements(deployed_models) as d
where
d ->> 'endpoint' = 'projects/123456789/endpoints/123456789';
select
name,
display_name,
json_extract(deployed_models, '$[*].endpoint') as endpoint,
json_extract(deployed_models, '$[*].deployed_model_id') as deployed_model_id
from
gcp_vertex_ai_model
where
json_extract(deployed_models, '$[*].endpoint') = 'projects/123456789/endpoints/123456789';
Explore models that support 'csv' format for input storage, gaining insights into the models that can process data in this format. This can be useful for managing and optimizing your data processing pipelines.
select
name,
display_name,
supported_input_storage_formats
from
gcp_vertex_ai_model
where
supported_input_storage_formats ? 'csv';
select
name,
display_name,
json_extract(supported_input_storage_formats, '$.csv') as supported_input_storage_formats
from
gcp_vertex_ai_model
where
json_type(supported_input_storage_formats, '$.csv') = 'string';