3723 docs rfccreate a doc showing how to integrate amazon sagemaker w…

…ith timescale cloud (#3789)

* chore: update the sagemaker doc

use-timescale/integrations/amazon-sagemaker.md

@@ -0,0 +1,147 @@
+---
+title: Integrate Amazon Sagemaker with Timescale Cloud
+excerpt: Integrate Amazon SageMaker with Timescale Cloud to store and analyze ML model data.
+products: [cloud, mst, self_hosted]
+keywords: [connect, integrate, amazon, aws, sagemaker]
+---
+
+import IntegrationPrereqs from "versionContent/_partials/_integration-prereqs.mdx";
+
+#  Integrate Amazon SageMaker with $CLOUD_LONG
+
+[Amazon SageMaker AI][Amazon Sagemaker] is a fully managed machine learning (ML) service. With SageMaker AI, data 
+scientists and developers can quickly and confidently build, train, and deploy ML models into a production-ready 
+hosted environment.
+
+This page shows you how to integrate Amazon Sagemaker with a $SERVICE_LONG.
+
+## Prerequisites
+
+<IntegrationPrereqs />
+
+* Setup an [AWS Account][aws-sign-up]
+
+## Prepare your $SERVICE_LONG to ingest data from SageMaker
+
+Create a table in $SERVICE_LONG to store model predictions generated by SageMaker.
+
+<Procedure>
+
+1. **Connect to your $SERVICE_LONG**
+
+   For $CLOUD_LONG, open an [SQL editor][run-queries] in [$CONSOLE][open-console]. For self-hosted, use [`psql`][psql].
+
+   ```sql
+   CREATE TABLE model_predictions (
+         time TIMESTAMPTZ NOT NULL,
+         model_name TEXT NOT NULL,
+         prediction DOUBLE PRECISION NOT NULL
+   );
+   ```
+
+1. **For better performance and easier real-time analytics, convert the table to a hypertable**
+
+   [Hypertables][about-hypertables] are PostgreSQL tables that automatically partition your data by time. You interact
+   with hypertables in the same way as regular PostgreSQL tables, but with extra features that makes managing your
+   time-series data much easier.
+
+   ```sql
+   SELECT create_hypertable('model_predictions', 'time');
+   ```
+
+</Procedure>
+
+## Create the code to inject data into a $SERVICE_LONG
+
+<Procedure>
+
+1. **Create a SageMaker Notebook instance**
+
+   1. In [Amazon SageMaker > Notebooks and Git repos][aws-notebooks-git-repos], click `Create Notebook instance`.
+   1. Follow the wizard to create a default Notebook instance.
+
+1. **Write a Notebook script that inserts data into your $SERVICE_LONG**   
+
+   1. When your Notebook instance is `inService,` click `Open JupyterLab` and click `conda_python3`. 
+   1. Update the following script with your [connection details][connection-info], then paste it in the Notebook.
+
+      ```python
+      import psycopg2
+      from datetime import datetime
+
+      def insert_prediction(model_name, prediction, host, port, user, password, dbname):
+            conn = psycopg2.connect(
+               host=host,
+               port=port,
+               user=user,
+               password=password,
+               dbname=dbname
+            )
+            cursor = conn.cursor()
+
+            query = """
+               INSERT INTO model_predictions (time, model_name, prediction)
+               VALUES (%s, %s, %s);
+            """
+
+            values = (datetime.utcnow(), model_name, prediction)
+            cursor.execute(query, values)
+            conn.commit()
+
+            cursor.close()
+            conn.close()
+
+      # Example usage
+      insert_prediction(
+            model_name="example_model",
+            prediction=0.95,
+            host="<host>",
+            port="<port>",
+            user="<user>",
+            password="<password>",
+            dbname="<dbname>"
+      )
+      ```
+
+1. **Test your SageMaker script**
+
+   1. Run the script in your SageMaker notebook.
+   1. Verify that the data is in your $SERVICE_SHORT
+
+      Open an [SQL editor][run-queries] and check the `sensor_data` table:
+
+      ```sql
+      SELECT * FROM model_predictions;
+      ```
+      You see something like:
+
+      |time	| model_name | prediction |
+      | -- | -- | -- |
+      |2025-02-06 16:56:34.370316+00|	timescale-cloud-model|	0.95|
+
+</Procedure>
+
+Now you can seamlessly integrate Amazon SageMaker with $CLOUD_LONG to store and analyze time-series data generated by 
+machine learning models. You can also untegrate visualization tools like [Grafana][grafana-integration] or 
+[Tableau][tableau-integration] with $CLOUD_LONG to create real-time dashboards of your model predictions.
+
+
+
+
+
+
+[Amazon Sagemaker]: https://docs.aws.amazon.com/sagemaker/latest/dg/whatis.html
+[aws-sign-up]: https://signin.aws.amazon.com/signup?request_type=register
+[install-aws-cli]: https://docs.aws.amazon.com/cli/latest/userguide/install-cliv2.html
+[install-python]: https://www.python.org/downloads/
+[install-postgresql]: https://www.postgresql.org/download/
+[console]: https://console.cloud.timescale.com/
+[grafana-integration]: use-timescale/:currentVersion:/integrations/grafana/
+[tableau-integration]: use-timescale/:currentVersion:/integrations/tableau/
+[run-queries]: /getting-started/:currentVersion:/run-queries-from-console/
+[open-console]: https://console.cloud.timescale.com/dashboard/services
+[psql]: /use-timescale/:currentVersion:/integrations/psql/
+[about-hypertables]: /use-timescale/:currentVersion:/hypertables/about-hypertables/
+[aws-notebooks-git-repos]:https://console.aws.amazon.com/sagemaker/home#/notebooks-and-git-repos
+[secure-vpc-aws]: /use-timescale/:currentVersion:/vpc/
+[connection-info]: /use-timescale/:currentVersion:/integrations/find-connection-details/

use-timescale/integrations/apache-airflow.md

@@ -21,8 +21,8 @@ This page shows you how to use a Python connector in a DAG to integrate Apache A
 
 <IntegrationPrereqs />
 
-* [Install Python3 and pip3][install-python-pip] 
-* [Install Apache Airflow][install-apache-airflow]
+* Install [Python3 and pip3][install-python-pip] 
+* Install [Apache Airflow][install-apache-airflow]
 
    Ensure that your Airflow instance has network access to $CLOUD_LONG.
 

use-timescale/integrations/azure-data-studio.md

@@ -51,5 +51,5 @@ You have successfully integrated Azure Data Studio with $CLOUD_LONG.
 [connection-info]: /use-timescale/:currentVersion:/integrations/find-connection-details/
 [azure-data-studio]: https://azure.microsoft.com/en-us/products/data-studio
 [ssl-mode]: /use-timescale/:currentVersion:/security/strict-ssl/
-
+[connection-info]: /use-timescale/:currentVersion:/integrations/find-connection-details/
 

use-timescale/integrations/index.md

@@ -47,10 +47,11 @@ Some of the most in-demand integrations for $CLOUD_LONG are listed below, with l
 
 ## Data engineering and extract, transform, load
 
-|               Name               | Description                                                                         |
-|:--------------------------------:|-------------------------------------------------------------------------------------|
-| [Apache Airflow][apache-airflow] | Programmatically author, schedule, and monitor workflows.                           |
-|[AWS Lambda][aws-lambda]| Run code without provisioning or managing servers, scaling automatically as needed. |
+|               Name               | Description                                                                   |
+|:--------------------------------:|-------------------------------------------------------------------------------|
+| [Amazon SageMaker][amazon-sagemaker]| Build, train, and deploy ML models into a production-ready hosted environment |
+| [Apache Airflow][apache-airflow] | Programmatically author, schedule, and monitor workflows.                     |
+| [AWS Lambda][aws-lambda]| Run code without provisioning or managing servers, scaling automatically as needed. |
 
 
 [psql]: /use-timescale/:currentVersion:/integrations/psql/
@@ -67,3 +68,4 @@ Some of the most in-demand integrations for $CLOUD_LONG are listed below, with l
 [aws-lambda]: /use-timescale/:currentVersion:/integrations/aws-lambda
 [postgresql-integrations]: https://slashdot.org/software/p/PostgreSQL/integrations/
 [prometheus]: /use-timescale/:currentVersion:/integrations/prometheus
+[amazon-sagemaker]: /use-timescale/:currentVersion:/integrations/amazon-sagemaker

use-timescale/page-index/page-index.js

@@ -778,6 +778,11 @@ module.exports = [
             href: "find-connection-details",
             excerpt: "Learn about connecting to your Timescale database",
           },
+          {
+            title: "Amazon SageMaker",
+            href: "amazon-sagemaker",
+            excerpt: "Integrate Amazon SageMaker with Timescale Cloud",
+          },
           {
             title: "Apache Airflow",
             href: "apache-airflow",