AWS Bedrock LAB

 AWS Bedrock is a fully managed service that makes it easy to build and deploy foundation models into your applications. It provides access to a variety of foundation models, including text-to-image, text-to-text, and code generation models.

Prerequisites

• An AWS account
• Basic understanding of AWS services

Step-by-Step Guide

1. Create a Bedrock Application

• Launch Bedrock: In the AWS Management Console, search for "Bedrock" and launch the service.
• Create Application: Click on "Create application".
• Provide Application Details: Enter a name for your application and select the desired foundation model.

AWS Bedrock Create Application screen

2. Configure Application Settings

• Configure Settings: Configure the application settings, such as the input and output formats.

AWS Bedrock Configure Application Settings screen

3. Create an Endpoint

• Create Endpoint: Create an endpoint to access your application.

AWS Bedrock Create Endpoint screen

4. Make API Calls

• Make API Calls: Use the AWS SDK or the Bedrock API to make calls to your application endpoint.

Python

import boto3

# Create a Bedrock client
client = boto3.client('bedrock')

# Make a call to your application endpoint
response = client.invoke_application(
    ApplicationId='your-application-id',
    Input={
        'Text': 'This is a prompt for the foundation model'
    }
)

5. Analyze Results

• Analyze Results: Analyze the output of your application to evaluate the performance of the foundation model.

Additional Considerations

• Foundation Models: Choose the appropriate foundation model for your use case.
• Customization: Customize the foundation model using fine-tuning or prompt engineering techniques.
• Deployment: Deploy your application to a production environment using AWS Lambda or other services.
• Monitoring: Monitor the performance of your application and the foundation model using CloudWatch.

AWS Augmented AI LAB

 AWS Augmented AI is a service that helps you build and deploy applications with human-in-the-loop capabilities. It allows you to integrate human expertise into your machine learning models to improve accuracy and address biases.

Prerequisites

• An AWS account
• Basic understanding of AWS services

Step-by-Step Guide

1. Create a Human Loop Job

• Launch Augmented AI: In the AWS Management Console, search for "Augmented AI" and launch the service.
• Create Job: Click on "Create job".
• Provide Job Details: Enter a name for your job, select the desired workflow, and configure the input and output settings.

AWS Augmented AI Create Job screen

2. Define Workflow

• Define Workflow: Define the workflow for your job, specifying the steps involved and the actions to be taken by humans and machines.

AWS Augmented AI Define Workflow screen

3. Configure Human Interface

• Configure Interface: Customize the human interface for your job, providing instructions and guidance for the human reviewers.

AWS Augmented AI Configure Human Interface screen

4. Create Job

• Create Job: Once you have configured your job, click on "Create job".

5. Submit Data

• Submit Data: Submit the data to be processed by the job.

6. Monitor Job

• Monitor Job: Track the progress of your job and review the results.

AWS Augmented AI Monitor Job screen

Additional Considerations

• Workflows: Create custom workflows to tailor the human-in-the-loop process to your specific needs.
• Human Interface: Design a user-friendly human interface to improve the efficiency and accuracy of human reviews.
• Quality Control: Implement quality control measures to ensure the accuracy of human reviews.
• Integration: Integrate Augmented AI with other AWS services like SageMaker and Rekognition for end-to-end machine learning workflows.

AWS Incident Manager LAB

 

AWS Incident Manager is a service that helps you manage and resolve incidents in your AWS environment. It provides a centralized console to track incidents, coordinate response teams, and communicate with stakeholders.

Prerequisites

• An AWS account
• Basic understanding of AWS services

Step-by-Step Guide

1. Create an Incident Manager Response Plan

• Launch Incident Manager: In the AWS Management Console, search for "Incident Manager" and launch the service.
• Create Response Plan: Click on "Create response plan".
• Define Response Plan: Define the steps and procedures to follow when responding to incidents.

AWS Incident Manager Create Response Plan screen

2. Create an Incident

• Create Incident: When an incident occurs, create an incident in Incident Manager.
• Provide Incident Details: Provide details about the incident, such as the root cause, impact, and response team.

AWS Incident Manager Create Incident screen

3. Manage Incident

• Manage Incident: Use the Incident Manager console to track the progress of the incident, assign tasks to response teams, and communicate with stakeholders.

AWS Incident Manager Manage Incident screen

4. Coordinate Response Teams

• Coordinate Teams: Use Incident Manager to coordinate the activities of different response teams involved in the incident.

5. Communicate with Stakeholders

• Communicate with Stakeholders: Use Incident Manager to communicate with stakeholders about the incident, its impact, and the status of the response.

6. Close Incident

• Close Incident: Once the incident is resolved, close it in Incident Manager.

Additional Considerations

• Customizations: Customize Incident Manager to meet your organization's specific needs.
• Integrations: Integrate Incident Manager with other AWS services like CloudWatch and SNS for automated notifications.
• Automation: Automate incident response processes using AWS Lambda and other services.

AWS Health Dashboard LAB

 AWS Health Dashboard is a service that provides real-time information about the health of AWS services and infrastructure. It helps you monitor the status of AWS resources and identify any potential issues that may impact your applications.

Prerequisites

• An AWS account
• Basic understanding of AWS services

Step-by-Step Guide

1. Access Health Dashboard

• Launch Health Dashboard: In the AWS Management Console, search for "Health Dashboard" and launch the service.

AWS Health Dashboard launch screen

2. View Overview

• View Overview: The Health Dashboard will provide an overview of the health of AWS services and infrastructure. This includes information about any current service disruptions or planned maintenance events.

AWS Health Dashboard overview screen

3. View Service Health

• View Service Health: Drill down into specific AWS services to view their health status in more detail.

AWS Health Dashboard service health screen

4. View Region Health

• View Region Health: Check the health status of specific AWS regions.

AWS Health Dashboard region health screen

5. View Event Details

• View Event Details: If there are any service disruptions or planned maintenance events, you can click on them to view more details.

AWS Health Dashboard event details screen

6. Set Up Notifications

• Set Up Notifications: Configure notifications to be alerted of any health events that may impact your applications.

AWS Health Dashboard notifications settings screen

Additional Considerations

• Custom Dashboards: Create custom dashboards to monitor the health of specific resources or services.
• Integration: Integrate Health Dashboard with other AWS services like CloudWatch and Config for comprehensive monitoring and governance.

AWS Grafana LAB

 AWS Grafana is a managed service that provides a powerful, open-source analytics and visualization platform. It allows you to create custom dashboards to monitor and analyze your AWS resources and applications.

Prerequisites

• An AWS account
• Basic understanding of AWS services

Step-by-Step Guide

1. Create a Grafana Workspace

• Launch Grafana: In the AWS Management Console, search for "Grafana" and launch the service.
• Create Workspace: Click on "Create workspace".
• Provide Workspace Details: Enter a name for your workspace and select the desired configuration settings.

AWS Grafana Create Workspace screen

2. Configure Data Sources

• Configure Data Sources: Connect Grafana to your AWS resources by configuring data sources (e.g., CloudWatch, DynamoDB, Kinesis).

AWS Grafana Configure Data Source screen

3. Create Dashboards

• Create Dashboards: Use the Grafana interface to create custom dashboards that visualize your data.
• Add Panels: Add panels to your dashboards to display metrics, charts, and other visualizations.

AWS Grafana Create Dashboard screen

4. Customize Dashboards

• Customize Dashboards: Customize the appearance and behavior of your dashboards to meet your specific needs.

AWS Grafana Customize Dashboard screen

5. Share Dashboards

• Share Dashboards: Share your dashboards with other users or teams.

Additional Considerations

• Integrations: Grafana supports integrations with a wide range of data sources and services.
• Customization: Customize Grafana to meet your specific requirements using plugins and themes.
• Alerts: Set up alerts to be notified of critical events or anomalies.
• Collaboration: Collaborate with other users to create and manage dashboards.

AWS Config LAB

 AWS Config is a service that provides a way to track the configuration of your AWS resources. It helps you ensure that your resources are configured according to your desired state and detect any unauthorized changes.

Prerequisites

• An AWS account
• Basic understanding of AWS services

Step-by-Step Guide

1. Create a Configuration Recorder

• Launch Config: In the AWS Management Console, search for "Config" and launch the service.
• Create Recorder: Click on "Create recorder".
• Provide Recorder Details: Enter a name for your recorder and select the desired configuration settings (e.g., bucket, role).

AWS Config Create Recorder screen

2. Create a Delivery Channel

• Create Delivery Channel: Create a delivery channel to specify where the configuration data will be stored.

AWS Config Create Delivery Channel screen

3. Start Recording

• Start Recording: Start the configuration recorder to begin tracking your resource configurations.

4. View Configuration History

• View History: Use the Config console to view the configuration history of your resources.

AWS Config Configuration History screen

5. Create Compliance Rules

• Create Rules: Create compliance rules to define the desired configuration state for your resources.

AWS Config Create Compliance Rule screen

6. Assess Compliance

• Assess Compliance: Config will automatically assess your resources against the defined compliance rules.

AWS Config Compliance screen

7. Take Action

• Take Action: If resources are not compliant, take the necessary steps to bring them into compliance.

Additional Considerations

• Custom Rules: Create custom rules to define specific configuration requirements.
• Data Retention: Set data retention policies for your configuration data.
• Integration: Integrate Config with other AWS services like CloudTrail and CloudWatch for comprehensive monitoring and governance.

AWS Compute Optimizer LAB

 AWS Compute Optimizer is a service that analyzes your EC2 instances and provides recommendations to optimize their performance and cost. It helps you identify underutilized instances, rightsize your instances, and choose the most cost-effective instance types.

Prerequisites

• An AWS account
• Running EC2 instances

Step-by-Step Guide

1. Enable Compute Optimizer

• Launch Compute Optimizer: In the AWS Management Console, search for "Compute Optimizer" and launch the service.
• Enable Compute Optimizer: Enable Compute Optimizer for your account.

AWS Compute Optimizer Enable screen

2. View Recommendations

• View Recommendations: After Compute Optimizer analyzes your EC2 instances, you can view the recommendations in the console.

AWS Compute Optimizer Recommendations screen

3. Analyze Recommendations

• Analyze Recommendations: Review the recommendations provided by Compute Optimizer. The recommendations will include suggestions for rightsizing instances, choosing more cost-effective instance types, and optimizing instance configurations.

4. Take Action

• Take Action: Based on the recommendations, you can take the following actions:
  • Rightsize Instances: Modify the instance type of underutilized instances.
  • Optimize Configurations: Adjust instance configurations (e.g., CPU credits, memory, storage) to improve performance and cost-efficiency.
  • Choose More Cost-Effective Instances: Select more cost-effective instance types for your workloads.

5. Monitor and Refine

• Monitor and Refine: Continuously monitor your EC2 instances and refine your optimization strategies based on the recommendations provided by Compute Optimizer.

Additional Considerations

• Custom Metrics: Provide custom metrics to Compute Optimizer for more accurate recommendations.
• Scheduling: Schedule Compute Optimizer to analyze your instances at regular intervals.
• Integration: Integrate Compute Optimizer with other AWS services like CloudWatch and Cost Explorer for comprehensive monitoring and cost analysis.