Table: oci_ai_anomaly_detection_model - Query OCI AI Anomaly Detection Models using SQL

OCI AI Anomaly Detection is a service within Oracle Cloud Infrastructure that enables users to detect anomalies in their data by using machine learning. It allows users to build and manage models that can be used for anomaly detection, providing a way to easily identify outliers in complex datasets. This service is particularly useful for applications such as fraud detection, fault detection, and operational optimization.

Table Usage Guide

The oci_ai_anomaly_detection_model table provides insights into AI anomaly detection models within Oracle Cloud Infrastructure. As a data scientist or AI engineer, you can explore model-specific details through this table, including model configurations, training data summaries, and associated metadata. Use it to uncover information about models, such as their status, the type of algorithm used, and the time of the last update.

Examples

Basic info

Explore which anomaly detection models are currently active within your project. This allows you to assess their lifecycle states and details, providing insights into the performance and health of each model.

select
  id,
  display_name,
  project_id,
  description,
  lifecycle_details,
  lifecycle_state as state
from
  oci_ai_anomaly_detection_model;

select
  id,
  display_name,
  project_id,
  description,
  lifecycle_details,
  lifecycle_state as state
from
  oci_ai_anomaly_detection_model;

List the models created in the last 30 days

Uncover the details of recently created AI anomaly detection models within the last month. This is useful for keeping track of new additions and understanding their lifecycle details and states.

select
  id,
  display_name,
  project_id,
  description,
  lifecycle_details,
  lifecycle_state as state
from
  oci_ai_anomaly_detection_model
where
  time_created >= now() - interval '30' day;

select
  id,
  display_name,
  project_id,
  description,
  lifecycle_details,
  lifecycle_state as state
from
  oci_ai_anomaly_detection_model
where
  time_created >= datetime('now', '-30 day');

List models which are not active

Determine the areas in which AI anomaly detection models are not currently active. This can be useful for identifying models that may be outdated or no longer in use, enabling more efficient resource management.

select
  id,
  display_name,
  project_id,
  description,
  lifecycle_details,
  lifecycle_state as state
from
  oci_ai_anomaly_detection_model
where
  lifecycle_state <> 'ACTIVE';

select
  id,
  display_name,
  project_id,
  description,
  lifecycle_details,
  lifecycle_state as state
from
  oci_ai_anomaly_detection_model
where
  lifecycle_state <> 'ACTIVE';

List the project details the model is associated to

Discover the segments that link a specific model to its associated projects in the AI anomaly detection system. This allows for a comprehensive overview of project details, aiding in project management and anomaly detection model utilization.

select
  m.id as data_asset_id,
  m.display_name as data_asset_name,
  p.id as project_id,
  p.time_created as project_created_time,
  p.display_name as project_name,
  p.lifecycle_state as project_lifecycle_state
from
  oci_ai_anomaly_detection_model as m
  left join oci_ai_anomaly_detection_project as p on m.project_id = p.id;

select
  m.id as data_asset_id,
  m.display_name as data_asset_name,
  p.id as project_id,
  p.time_created as project_created_time,
  p.display_name as project_name,
  p.lifecycle_state as project_lifecycle_state
from
  oci_ai_anomaly_detection_model as m
  left join oci_ai_anomaly_detection_project as p on m.project_id = p.id;

This table is available as a standalone Exporter CLI. Steampipe exporters are stand-alone binaries that allow you to extract data using Steampipe plugins without a database.

You can download the tarball for your platform from the Releases page, but it is simplest to install them with the steampipe_export_installer.sh script:

/bin/sh -c "$(curl -fsSL https://steampipe.io/install/export.sh)" -- oci

You can pass the configuration to the command with the --config argument:

steampipe_export_oci --config '<your_config>' oci_ai_anomaly_detection_model