1. End-to-End AI Pipelines Using Databricks Notebooks


    Creating end-to-end AI pipelines with Databricks involves integrating various components such as notebooks for development, model serving for deployment, and pipelines to manage data and processing workflows. Using Pulumi, you can automate the provisioning and management of these components on Databricks platform. Below, I'll show you how to use Pulumi to create a Databricks notebook, and I'll also mention other components you can include to build a complete pipeline.

    Creating a Databricks Notebook

    A Databricks notebook is an interactive environment where you can write code in various languages, visualize data, and build AI models. With Pulumi, you can define a Databricks notebook as a resource in your infrastructure code, specifying its path, content, and other settings.

    Below is a program written in Python that demonstrates how to create a Databricks notebook using Pulumi:

    import pulumi import pulumi_databricks as databricks # Create a Databricks Notebook. notebook = databricks.Notebook("ai-notebook", content_base64="cHJpbnQoIkhlbGxvLCB3b3JsZCEiKQ==", # Base64-encoded content, in this case a simple Python print statement path="/Users/pulumi-user/ai-notebook", # The path where the notebook will be stored in the workspace language=databricks.NotebookLanguage.PYTHON, # The language of the notebook, e.g., PYTHON, SCALA, SQL, R, or MARKDOWN ) # Export the notebook URL so it can be accessed later. pulumi.export("notebook_path", notebook.path)

    In the above program, we import the necessary Pulumi modules and then define a single resource, which is a Databricks notebook. We specify the notebook's content in base64 encoding, the path where the notebook will live, and the programming language of the notebook.

    Below are some additional resources you might want to include in an end-to-end AI pipeline:

    • Model Serving: To serve machine learning models built within Databricks notebooks, you can use the databricks.ModelServing resource. This component allows you to deploy and serve prediction endpoints that can be used by applications.

      Read more about databricks.ModelServing

    • Pipeline: Managing data workflows can be done through databricks.Pipeline. It organizes the flow of data and can be used to automate data transformation, aggregation, and other data processes.

      Read more about databricks.Pipeline

    • Table: AI pipelines often involve interacting with data stored in tables. You can manage and automate the creation of tables in Databricks with databricks.Table.

      Read more about databricks.Table

    When building your AI pipeline with Pulumi, you will define these resources in your codebase, configure them as needed, and use Pulumi's CLI to deploy them to the Databricks environment. This approach ensures your infrastructure is versioned, repeatable, and maintainable.

    The real power of using Pulumi for your AI pipelines is the ability to integrate this infrastructure definition within your CI/CD pipelines, enabling automated deployments and updates to your AI environment. With these practices, you can focus on building the AI models and trust Pulumi to manage the underlying infrastructure reliably.