1. Storing Time-Series Data for ML Model Training


    Storing time-series data efficiently is crucial for machine learning model training, as the performance and accuracy of ML models can be significantly influenced by the quality and form of the underlying data. Time-series data is typically a sequence of data points collected or recorded at regular time intervals and is common in various domains such as finance, IoT, and health care.

    To store time-series data for ML model training, we will use AWS services, as AWS provides robust solutions for handling time-series data. We will use Amazon Timestream, which is a fast, scalable, and serverless time-series database service for IoT and operational applications. With Timestream, you can easily store and analyze trillions of events per day at one-tenth the cost of relational databases.

    Below is a Pulumi program in Python that creates a Timestream database and table. This setup is ideal for ingesting, storing, and querying time-series data that you can later use for training machine learning models.

    import pulumi import pulumi_aws as aws # Create an AWS Timestream database for storing time-series data. # Refer to https://www.pulumi.com/registry/packages/aws/api-docs/timestreamwrite/database/ for more details. time_series_database = aws.timestreamwrite.Database("timeSeriesDatabase", # Optionally, you can specify KMS Key ID for encryption; if not specified, AWS managed KMS key will be used. # kms_key_id="your-kms-key-id" ) # Create a Timestream table within the database. # Time-series data in Timestream is stored in tables. A table is a collection of time-series data with the same retention properties. # Refer to https://www.pulumi.com/registry/packages/aws/api-docs/timestreamwrite/table/ for more details. time_series_table = aws.timestreamwrite.Table("timeSeriesTable", database_name=time_series_database.name, retention_properties={ "memory_store_retention_period_in_hours": 24, "magnetic_store_retention_period_in_days": 7 } # You can specify additional properties such as tags, magnetic_store_write_properties etc. ) # Output the names of the database and table which can be used as references in other parts of your infrastructure or application. pulumi.export("database_name", time_series_database.name) pulumi.export("table_name", time_series_table.name)

    In the above program, we first create a Timestream database, which serves as a container for Timestream tables and is used to manage the retention properties of the data. The kms_key_id is optional and if not specified, Timestream utilizes the AWS managed KMS key to encrypt the data at rest.

    Next, we create a table within the Timestream database. This table holds the actual time-series data, and we define retention properties to specify how long the data should be kept in the memory store and magnetic store. The memory store provides faster access to recent data, while the magnetic store provides cost-efficient storage for older data.

    The retention properties are configured depending on the needs of the machine learning models – how much historical data they need to access quickly, and how much can be archived. In this example, we keep data in the memory store for 24 hours and in the magnetic store for 7 days.

    Finally, we export the names of the created database and table as stack outputs. These outputs can be used in other parts of your cloud infrastructure or directly in your applications to write and read time-series data.

    Use this Pulumi program as a starting point to create a scalable infrastructure for storing your ML time-series data on AWS. You can further modify the program to accommodate your specific use cases, such as adding more tables, configuring additional database properties, or provisioning other AWS services that integrate with Timestream.