# Serverless Workflows
Serverless

Amazon SageMaker Unified Studio supports serverless workflows powered by [Amazon MWAA Serverless](https://docs.aws.amazon.com/mwaa/latest/mwaa-serverless-userguide/what-is-mwaa-serverless.html). With serverless workflows, you can orchestrate data processing tasks without provisioning or managing Apache Airflow infrastructure. Resources are automatically provisioned when a workflow runs and released when it completes, so you only pay for actual workflow run time.

Serverless workflows in Amazon SageMaker Unified Studio provide the following capabilities:
+ Automatic scaling of compute resources to meet workflow demands without manual intervention.
+ Workflow isolation, where each workflow runs with its own execution role and worker, providing enhanced security and preventing cross-workflow interference.
+ Cost optimization through a pay-per-use model with no upfront costs or minimum provisioning required.
+ Built-in integration with AWS services including Amazon S3, Amazon Redshift, Amazon EMR, AWS Glue, and Amazon SageMaker AI.
+ YAML-based workflow definition files.

# Using serverless visual workflows
Visual workflows

With Amazon SageMaker Unified Studio serverless visual workflows, you can create and orchestrate tasks using an intuitive drag-and-drop interface without writing code. Visual workflows supports over 80 tasks to help interact with multiple AWS services and automate use-cases across analytics, compute, catalog and storage. For the full list of supported tasks, see [Supported operators](https://docs.aws.amazon.com/mwaa/latest/mwaa-serverless-userguide/operators.html).
+ [Create a serverless visual workflow](#serverless-create-visual-workflow)
+ [View visual workflows code](#serverless-view-visual-workflow-code)
+ [Monitor your workflow](#serverless-monitor-visual-workflow)
+ [Converting existing Airflow DAGs](#serverless-convert-airflow-dags)

## Create a serverless visual workflow
Create a visual workflow

Use visual workflows to orchestrate tasks in your project. With visual workflows, you can define a collection of tasks organized as a directed acyclic graph (DAG) that can run on a user-defined schedule.

**To create a visual workflow**

1. Log in to Amazon SageMaker Unified Studio.

1. In the left navigation pane, choose **Workflows**.

1. Choose **Create new workflow** to open the Visual Workflows editor.

1. Provide a name to your workflow and choose **Save**.

1. In the Find tasks search window under Add tasks, choose a task to add to your workflow. The selected task appears in the canvas.

1. Configure the task by giving it a name and editing the prepopulated fields.

1. Choose the **\$1** symbol to add more tasks. You can drag the tasks to fit your workflow.

1. Complete the workflow by connecting the tasks. To connect the tasks, choose the **\$1** symbol of one task to the **\$1** symbol of another task. The arrows represent the execution order and data flow.

1. Once you've created your workflow, you can configure its settings. Choose the settings gear.

   1. In the Workflow settings tab you can:
      + Edit the Workflow name if the workflow has never been saved to a project.
      + Provide an optional description to the workflow.
      + Toggle the Run on schedule button and set the Schedule status to Active or Paused.
      + Choose an option from the Schedule dropdown menu to set a schedule for your workflow or specify a CRON expression in the Start date and time in UTC and End date and time in UTC fields below.

   1. Once the settings are set, choose **Apply** to save them.

   1. In the Default parameters tab, choose **Add parameter** and provide a name and a default value to the parameter and choose **Apply** to save them.

   1. In the Tags tab, choose **Add tag** to create an airflow tag to your workflow and provide a name to the tag, then choose **Apply** to save it. Airflow tags help in filtering the workflows. This step is optional.

1. Choose **Save** to save the current workflow. If there are any validation errors, the notifications symbol next to the settings gear will show a number next to it which indicates the number of errors. You must fix them before you can successfully run the workflow.

## View visual workflows code
View workflow code

To view a visual workflow code, navigate to the workflow details page by selecting a workflow from the Workflows page list. Then choose the Actions dropdown menu and choose **View code**.

## Monitor your workflow
Monitor workflow

**To monitor your workflow**

1. From the Workflows view, choose the vertical dots to the far right of your workflow's name and select **View runs**.

1. In the subsequent Runs view you will see your workflow runs.

1. Choose the run to show the tasks.

1. Choose the task ID to show the task output and associated logs.

## Converting existing Airflow DAGs
Convert Airflow DAGs

You can convert existing Airflow workflows to YAML through a Python library. For more information, see [Introducing Amazon MWAA Serverless](https://aws.amazon.com/blogs/big-data/introducing-amazon-mwaa-serverless/) on the AWS Big Data Blog.