Integrate Amazon Sagemaker with Tiger

Amazon SageMaker is a fully managed machine learning service. Integrate Amazon SageMaker with Tiger Cloud to store and analyze ML model data

Amazon SageMaker AI 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 .

Prerequisites

To follow the steps on this page:

• Create a target with the Real-time analytics capability enabled.

  You need your connection details. This procedure also works for .

• Set up an AWS Account

Prepare your Tiger Cloud service to ingest data from SageMaker

Create a table in to store model predictions generated by SageMaker.

1. Connect to your

   For , open an SQL editor in . For , use psql.

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

   s are tables that automatically partition your data by time. You interact with s in the same way as regular 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
   );
   

   When you create a using CREATE TABLE … WITH …, the default partitioning column is automatically the first column with a timestamp data type. Also, creates a columnstore policy that automatically converts your data to the , after an interval equal to the value of the chunk_interval, defined through compress_after in the policy. This columnar format enables fast scanning and aggregation, optimizing performance for analytical workloads while also saving significant storage space. In the conversion, chunks are compressed by up to 98%, and organized for efficient, large-scale queries.

   You can customize this policy later using alter_job. However, to change after or created_before, the compression settings, or the the policy is acting on, you must remove the columnstore policy and add a new one.

   You can also manually convert chunks in a to the .

Create the code to inject data into a Tiger Cloud service

1. 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.

2. Write a Notebook script that inserts data into your

   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>"
      )
      

3. Test your SageMaker script

   1. Run the script in your SageMaker notebook.

   2. Verify that the data is in your

      Open an SQL editor and check the sensor_data table:

      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

Now you can seamlessly integrate Amazon SageMaker with to store and analyze time-series data generated by machine learning models. You can also untegrate visualization tools like Grafana or Tableau with to create real-time dashboards of your model predictions.