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pgml-cms/docs/introduction/apis/sql-extensions/pgml.deploy.md

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@@ -38,16 +38,15 @@ The default deployment behavior allows any algorithm to qualify. It's automatica
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### Deploying the best score
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\=== "SQL"
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#### SQL
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```sql
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SELECT * FROM pgml.deploy(
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'Handwritten Digit Image Classifier',
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<pre class="language-sql"><code class="lang-sql"><strong>SELECT * FROM pgml.deploy(
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</strong> 'Handwritten Digit Image Classifier',
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strategy => 'best_score'
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);
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```
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</code></pre>
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\=== "Output"
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#### Output
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```sql
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project | strategy | algorithm
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(1 row)
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```
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\===
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### **Specific Algorithms**
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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:
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\=== "SQL"
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#### SQL
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```sql
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SELECT * FROM pgml.deploy(
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);
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```
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\=== "Output"
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#### Output
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```sql
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project_name | strategy | algorithm
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(1 row)
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```
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\===
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### Rolling Back
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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.
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\=== "Rollback 1"
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#### Rollback
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```sql
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SELECT * FROM pgml.deploy(
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);
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```
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\=== "Output"
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#### Output
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```sql
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project | strategy | algorithm
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(1 row)
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```
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\=== "Rollback 2"
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#### Rollback again
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Rollbacks are actually new deployments, so issuing two rollbacks in a row will leave you back with the original model, making rollback safely undoable.
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```sql
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SELECT * FROM pgml.deploy(
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);
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```
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\=== "Output"
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#### Output
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```sql
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project | strategy | algorithm
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------------------------------------+----------+-----------
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Handwritten Digit Image Classifier | rollback | xgboost
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(1 row)
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```
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\===

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