1. Databricks for AI Model Training and Deployment Pipelines


    Databricks is a data analytics platform optimized for the Microsoft Azure and Amazon Web Services cloud infrastructure. It provides a collaborative environment for data scientists, data engineers, and business analysts to work together. The platform is based on Apache Spark, which allows for handling large-scale data processing.

    To set up AI Model Training and Deployment Pipelines with Databricks, you'll typically go through the following steps:

    1. Create a Databricks Workspace: This is where all your notebooks, datasets, and models live.

    2. Provision a Databricks Cluster: Clusters are computational resources in Databricks, where you'll run your analysis, train models, and so on.

    3. Interact with Databricks File System (DBFS): It's a file system you can use within Databricks to store data, models, and intermediary outputs.

    4. Develop Notebooks: Databricks notebooks are where you write Python (or Scala, R, etc.) code to analyze your data and train machine learning models.

    5. Schedule Jobs for Training and Deployment: You'll use jobs to automate the training and deployment of machine learning models.

    Below is a simplified Pulumi program in Python that sets up a basic workflow for AI Model Training and Deployment Pipelines with Databricks using the pulumi_databricks provider.

    The example program will:

    • Create a Databricks Workspace.
    • Provision a basic cluster for workloads.
    • Establish a job to represent the training pipeline.
    import pulumi import pulumi_databricks as databricks # Create a Databricks workspace workspace = databricks.Workspace("ai_training_workspace", # Parameters such as SKU, location and others can be specified here. # For the sake of example, we'll use default or minimal configuration. # Refer to the Pulumi docs for additional parameters and details: # https://www.pulumi.com/registry/packages/databricks/api-docs/workspace/ ) # Provision a Databricks cluster within the workspace cluster = databricks.Cluster("ai_training_cluster", cluster_name="training-cluster", spark_version="latest", # specify the spark version node_type_id="Standard_D3_v2", # specify the node type autotermination_minutes=20, # This setting is to minimize costs num_workers=2, # specify the initial number of worker nodes # For more details on configuring clusters, see the documentation: # https://www.pulumi.com/registry/packages/databricks/api-docs/cluster/ ) # Deploy a databricks notebook which will contain the model training code # The notebook can be populated by providing the `content_base64` or `source` parameter accordingly. notebook = databricks.Notebook("ai_model_training", path="/Users/your.email@example.com/MyNotebook", # content_base64="base64_encoded_content", language="PYTHON", # For more details on Notebooks, see the documentation: # https://www.pulumi.com/registry/packages/databricks/api-docs/notebook/ ) # Set up a Databricks Job for the training pipeline # You can define a set of tasks including which notebook to run and the schedule to run it. job = databricks.Job("ai_model_training_job", name="Model Training Job", new_cluster=cluster.id.apply(lambda cid: databricks.JobNewClusterArgs( # Specs for a new cluster cluster_id=cid, spark_version="latest", # match the version used with the cluster node_type_id="Standard_D3_v2", # Match the node type )), notebook_task=databricks.JobNotebookTaskArgs( notebook_path=notebook.path ), # For more details on Jobs, see the documentation: # https://www.pulumi.com/registry/packages/databricks/api-docs/job/ ) pulumi.export("workspace_url", workspace.workspace_url) pulumi.export("cluster_id", cluster.cluster_id) pulumi.export("job_id", job.job_id)

    This is a basic setup, and you might need to adjust the cluster size, node types, and notebook content based on the needs of your AI model training and deployment process. Additionally, Databricks allows integration with MLflow for experiment tracking and model deployment, which you might want to consider for a more comprehensive pipeline.

    Each resource created using Pulumi corresponds to a component in the Databricks platform that would help you manage and automate tasks for AI model training and deployment. Additional steps such as adding security groups, storage accounts, and more fine-grained permissions would be necessary for a production-ready environment.

    Remember that all resources created will incur costs according to the Databricks pricing model and that of the underlying cloud provider. Hence, it is very important to specify the autotermination_minutes attribute for clusters to ensure they do not run longer than needed.