Integrate Amazon SageMaker with Timescale Cloud

Connect to your Timescale Cloud service

For Timescale Cloud, open an SQL editor in Timescale Console. For self-hosted, use psql.

For better performance and easier real-time analytics, create a hypertable

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.

CREATE TABLE model_predictions (
  time TIMESTAMPTZ NOT NULL,
  model_name TEXT NOT NULL,
  prediction DOUBLE PRECISION NOT NULL
) WITH (
  tsdb.hypertable,
  tsdb.partition_column='time'
);

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If you are self-hosting TimescaleDB v2.19.3 and below, create a PostgreSQL relational table, then convert it using create_hypertable. You then enable hypercore with a call to ALTER TABLE.

Create a SageMaker Notebook instance

1. In Amazon SageMaker > Notebooks and Git repos, click Create Notebook instance.
2. Follow the wizard to create a default Notebook instance.

Write a Notebook script that inserts data into your Timescale Cloud service

1. When your Notebook instance is inService, click Open JupyterLab and click conda_python3.

2. Update the following script with your connection details, then paste it in the Notebook.

   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>"
   )

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Test your SageMaker script

1. Run the script in your SageMaker notebook.

2. Verify that the data is in your service

   Open an SQL editor and check the sensor_data table:

   SELECT * FROM model_predictions;

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   You see something like:

   time	model_name	prediction
   2025-02-06 16:56:34.370316+00	timescale-cloud-model	0.95