1. Change Data Capture for Event-driven Machine Learning Pipelines


    Change Data Capture (CDC) is a pattern that enables capturing changes in data so that action can be taken based on these changes. In the context of event-driven architectures and machine learning (ML) pipelines, CDC can be used to automatically trigger downstream processes—such as data transformation, model training, or inference—whenever new data is available or existing data is modified.

    Here, we'll create a sample Pulumi program that sets up a CDC pipeline using Azure services. We will make use of Azure Data Factory for orchestrating the data pipelines and Azure Machine Learning for managing the ML lifecycle.

    First, Azure Data Factory pipelines are created to move and transform data. For this, ChangeDataCapture resource from the azure-native.datafactory package will be used to configure CDC.

    Second, we will need a way to capture the events and pass them along. For this, Event Hubs or a similar messaging service could be used to capture the changes and deliver them to the machine learning pipeline. By using the messaging system as an intermediary, the system ensures that all changes are captured and can act as a buffer if the processing pipeline is temporarily overwhelmed or down.

    Finally, Azure Machine Learning resources will be set up to handle the ML lifecycle. We'll use the MachineLearningDataset resource from the azure-native.machinelearningservices package for managing datasets in Azure ML.

    The purpose of this Pulumi program is to set up the infrastructure required to capture data changes and trigger machine learning pipelines. The actual logic and specifics of data processing and model retraining are beyond the scope of this program and will need to be implemented within the data factory pipelines and Azure Machine Learning workspaces.

    Here's a basic structure of what our Pulumi program would look like:

    import pulumi import pulumi_azure_native.datafactory as datafactory import pulumi_azure_native.machinelearningservices as ml # Here we assume you have already defined resource group and data factory. # Replace the placeholders below with your actual resource group and factory names. resource_group_name = "my-resource-group" data_factory_name = "my-data-factory" # Configuring Change Data Capture in Azure Data Factory change_data_capture = datafactory.ChangeDataCapture("myCDC", factory_name=data_factory_name, resource_group_name=resource_group_name, # Define the Change Data Capture properties here policy=datafactory.ChangeDataCapturePolicyArgs( mode="Incremental", # Assuming incremental changes are captured # You need to set recurrence according to the needs of your pipeline. recurrence=datafactory.RecurrenceArgs( interval=1, frequency="Minute", ), ), # Source and target information to be defined as needed # sourceConnectionsInfo=... # targetConnectionsInfo=... ) # Creating a Machine Learning Dataset for ML pipelines ml_dataset = ml.MachineLearningDataset("myMLDataset", resource_group_name=resource_group_name, workspace_name="my-ml-workspace", # Define the dataset properties and source here # parameters=... ) # Exports URLs or names to be used later on pulumi.export('change_data_capture_name', change_data_capture.name) pulumi.export('machine_learning_dataset_name', ml_dataset.name)

    To use the code:

    1. Replace "my-resource-group", "my-data-factory", and "my-ml-workspace" with your actual Azure resource names.
    2. Define the source and target connections for the change data capture.
    3. Specify the dataset properties and source for Azure ML dataset according to your specifics.

    This Pulumi code creates the necessary infrastructure to monitor data changes and manage ML datasets. The actual data transformation, machine learning model training or scoring will have to be defined within Azure Data Factory's pipelines and linked to the datasets and ML workspaces that we have created with this Pulumi program.

    Remember to plan the tasks that run within Azure Data Factory and machine learning processes to properly utilize the infrastructure you set up. This includes defining the data transformations, model training, model evaluation, and possibly redeploying the model if there's a significant change in the data profile.