Amazon Augmented AI lab

 AWS Augmented AI is a service that helps you build and deploy AI applications more efficiently. It offers features like human-AI collaboration, automated workflows, and data labeling.

1. Prerequisites

• An AWS account
• A supported AWS region (check the AWS documentation for the latest list)

2. Create a Workspace

• Navigate to Augmented AI: In the AWS Management Console, search for "Augmented AI" and select the service.
• Create a workspace: Click on "Create workspace".
• Provide details: Give your workspace a name and description.
• Choose a team: Select the team that will be working on the workspace.
• Create the workspace: Click "Create workspace".

creating a workspace in AWS Augmented AI

3. Create a Workflow

• Select your workspace: Click on the workspace you just created.
• Create a workflow: Click on "Create workflow".
• Provide details: Give your workflow a name and description.
• Add steps: Add steps to your workflow, such as human review, machine learning model, or data labeling.
• Create the workflow: Click "Create workflow".

creating a workflow in AWS Augmented AI

4. Configure Workflow Steps

• Select your workflow: Click on the workflow you created.
• Edit steps: Click on the steps in your workflow to edit their configuration.
• Configure settings: Specify settings like input and output formats, machine learning models, and task instructions.

5. Add Data

• Select your workflow: Click on the workflow you created.
• Add data: Click on "Add data".
• Upload data: Upload the data you want to process.

6. Start the Workflow

• Select your workflow: Click on the workflow you created.
• Start workflow: Click on "Start workflow".

7. Monitor the Workflow

• View workflow progress: Monitor the progress of your workflow in the console.
• Review results: Review the results of the workflow, including human annotations and machine learning model predictions.

Additional Steps

• Integrate with other AWS services: Augmented AI can be integrated with other AWS services like SageMaker and Rekognition.
• Use custom models: Use your own machine learning models in your workflows.
• Manage teams and permissions: Manage teams and permissions within your workspace.