SHOW SNOWFLAKE.ML.ANOMALY_DETECTION¶

Lists all anomaly detection models.

Syntax¶

SHOW SNOWFLAKE.ML.ANOMALY_DETECTION [ LIKE <pattern> ];
  [ IN
      {
        ACCOUNT                  |

        DATABASE                 |
        DATABASE <database_name> |

        SCHEMA                   |
        SCHEMA <schema_name>     |
        <schema_name>
      }
   ]

Copy

Parameters¶

LIKE 'pattern'

Optionally filters the command output by object name. The filter uses case-insensitive pattern matching, with support for SQL wildcard characters (% and _).

For example, the following patterns return the same results:

... LIKE '%testing%' ...

... LIKE '%TESTING%' ...

. Default: No value (no filtering is applied to the output).

[ IN ... ]

Optionally specifies the scope of the command. Specify one of the following:

ACCOUNT

Returns records for the entire account.

DATABASE, . DATABASE db_name

Returns records for the current database in use or for a specified database (db_name).

If you specify DATABASE without db_name and no database is in use, the keyword has no effect on the output.

SCHEMA, . SCHEMA schema_name, . schema_name

Returns records for the current schema in use or a specified schema (schema_name).

SCHEMA is optional if a database is in use or if you specify the fully qualified schema_name (for example, db.schema).

If no database is in use, specifying SCHEMA has no effect on the output.

Default: Depends on whether the session currently has a database in use:

Database: DATABASE is the default (that is, the command returns the objects you have privileges to view in the database).
No database: ACCOUNT is the default (that is, the command returns the objects you have privileges to view in your account).

Output¶

Model properties and metadata in the following columns:

Column	Description
created_on	Date and time when the model was created
name	Name of the model
database_name	Database in which the model is stored
schema_name	Schema in which the model is stored
current_version	The version of the model algorithm
comment	Comment for the model
owner	The role that owns the model

Examples¶

For a representative example, see the anomaly detection example.