\=== "SQL"

SELECT*FROMpgml.deploy(

'Handwritten Digit Image Classifier',

<preclass="language-sql"><codeclass="lang-sql"><strong>SELECT * FROM pgml.deploy(

</strong> 'Handwritten Digit Image Classifier',

strategy => 'best_score'

);

\=== "Output"

project | strategy | algorithm

(1 row)

\===

Deployment candidates can be restricted to a specific algorithm by including the`algorithm` parameter. This is useful when you're training multiple algorithms using different hyperparameters and want to restrict the deployment a single algorithm only:

\=== "SQL"

SELECT*FROMpgml.deploy(

);

\=== "Output"

project_name | strategy | algorithm

(1 row)

In case the new model isn't performing well in production, it's easy to rollback to the previous version. A rollback creates a new deployment for the old model. Multiple rollbacks in a row will oscillate between the two most recently deployed models, making rollbacks a safe and reversible operation.

\=== "Rollback 1"

SELECT*FROMpgml.deploy(

);

\=== "Output"

project | strategy | algorithm

(1 row)

\=== "Rollback 2"

Rollbacks are actually new deployments, so issuing two rollbacks in a row will leave you back with the original model, making rollback safely undoable.

SELECT*FROMpgml.deploy(

);

\=== "Output"

project | strategy | algorithm

Handwritten Digit Image Classifier |rollback | xgboost

(1 row)

\===