1. Collaborative AI Model Development using Databricks Notebooks


    Collaborative AI model development using Databricks Notebooks typically involves creating and managing notebooks within a Databricks workspace, where team members can write code, execute it, and share results. Databricks is a platform that provides a collaborative environment for data science, data engineering, and business analytics, powered by Apache Spark.

    When automating the provisioning of resources for Databricks with Pulumi, we would use the pulumi_databricks package to create, manage, and deploy Databricks resources such as notebooks, clusters, and jobs. In the context of collaborative AI model development, we would create notebooks initially, which could be used by data scientists or engineers to write and share their models.

    The databricks.Notebook resource allows you to manage your notebooks programmatically in your Databricks workspace.

    Here's what a Pulumi program in Python might look like to create a Databricks notebook:

    import pulumi import pulumi_databricks as databricks # Create a Databricks Notebook. notebook = databricks.Notebook("my-collaborative-notebook", path="/Users/my.user@domain.com/my-collaborative-notebook", content_base64=pulumi.Output.secret("base64-encoded-content"), language=databricks.NotebookLanguage.PYTHON, ) # Export the URL of the notebook, which can be used to directly access it in Databricks. notebook_url = pulumi.Output.concat("https://", databricks.workspace_url, notebook.path) pulumi.export("notebook_url", notebook_url)

    In this program, we are creating a new Databricks notebook specified by:

    • path: The location within the Databricks workspace where the notebook will be placed.
    • content_base64: The base64 encoded content of the notebook file, which could be the initial content of your collaborative AI model script. This allows for the notebook to be created with predefined content.
    • language: The programming language for the notebook. Databricks supports multiple languages (e.g., Scala, Python, R, SQL); here we use Python, a common choice for data science and machine learning tasks.

    The notebook_url is a combination of the workspace URL and the notebook's path, creating a direct URL to the notebook in the Databricks environment. This URL can be shared with other team members for collaboration.

    Notice that in a real-world scenario, you would generate your base64-encoded content from a source file or through some other means to ensure confidentiality and consistency. Do not include sensitive content as plain text in your Pulumi code; instead, you should either use Pulumi's secrets management or some other secure mechanism to handle it.

    Remember to configure your Pulumi environment with the required Databricks credentials and workspace URL. Usually, this configuration would be part of setting up your Pulumi Databricks provider, and it's not shown in the provided example.

    After applying this Pulumi program, the resources described in it (in this case, a Databricks Notebook) will be created in your Databricks workspace, and you'll obtain an output URL which can be used by your team for collaborative work.