1. Predictive Issue Categorization with Machine Learning


    To create a predictive issue categorization system with machine learning on the cloud, you would typically use some managed machine learning services provided by cloud providers such as AWS, Azure, or Google Cloud. These services provide you with the tools required to build, train, and deploy machine learning models that can then be used to categorize issues based on their content.

    For example, on Azure, you might use Azure Machine Learning Services to create and manage your machine learning models. Pulumi provides resources that allow you to define and deploy these services using infrastructure as code (IaC). Let's go through setting up a basic Azure Machine Learning Workspace and a Compute Instance where you can develop and train a machine learning model to categorize issues.

    Here's a Pulumi Python program that will help you set this up:

    import pulumi import pulumi_azure_native as azure_native # Configuration variables for the Azure Machine Learning resources resource_group_name = 'ml_resource_group' workspace_name = 'ml_workspace' compute_name = 'ml_compute_instance' # Create an Azure Resource Group to hold the Machine Learning resources resource_group = azure_native.resources.ResourceGroup(resource_group_name) # Create an Azure Machine Learning Workspace ml_workspace = azure_native.machinelearningservices.Workspace( workspace_name, resource_group_name=resource_group.name, identity=azure_native.machinelearningservices.IdentityArgs( type='SystemAssigned', ), location=resource_group.location, sku=azure_native.machinelearningservices.SkuArgs( name="Basic", # Choose the SKU that best fits your needs ), ) # Create an Azure Machine Learning Compute Instance ml_compute = azure_native.machinelearningservices.Compute( compute_name, resource_group_name=resource_group.name, workspace_name=ml_workspace.name, properties=azure_native.machinelearningservices.ComputeArgs( compute_type="AmlCompute", properties=azure_native.machinelearningservices.AmlComputeArgs( vm_size="STANDARD_D2_V2", # Suitable for small to medium-sized models scale_settings=azure_native.machinelearningservices.ScaleSettingsArgs( max_node_count=1, ), vm_priority="dedicated", ), ), ) # Export the Azure Machine Learning Workspace URL pulumi.export('workspace_url', ml_workspace.workspace_url)

    This program sets up a new Azure Resource Group, Machine Learning Workspace, and a Compute Instance. These resources create an environment where you can train and host your machine learning models.

    • The azure_native.resources.ResourceGroup resource creates a new group in Azure to hold and organize other resources.
    • The azure_native.machinelearningservices.Workspace represents the Azure ML Workspace, which is the foundational piece for all machine learning activities. It tracks and manages all the models, experiments, and compute resources.
    • The azure_native.machinelearningservices.Compute resource is used to define a compute instance in the ML Workspace where the ML models can be trained and evaluated. The vm_size parameter indicates the virtual machine size that matches your model's complexity and training needs.

    This is a simplified example of part of the infrastructure. To categorize issues predictively, you would additionally need to:

    1. Develop a machine learning model using Azure ML or similar services.
    2. Train your model with existing labeled issue data.
    3. Deploy the model to a prediction endpoint.
    4. Create a process to feed issues into this model to get predictions.
    5. Integrate the predictions back into your issue tracking system.

    These steps would involve a deeper understanding of machine learning workflows and Azure ML's SDK or the portal UI for managing datasets, experiments, models, and endpoints. You would write additional Pulumi code for such resources once your data science workflows are ready to be deployed and managed through infrastructure as code.