AWS DeepComposer LAB

 AWS DeepComposer is a hands-on machine learning tool designed for developers and data scientists to experiment with and learn about generative AI. It provides a physical device and a cloud-based environment for training and deploying generative models.

Prerequisites

• An AWS account
• AWS DeepComposer device

Step-by-Step Guide

1. Set Up DeepComposer

• Connect to Wi-Fi: Connect your DeepComposer device to a Wi-Fi network.
• Sign In: Sign in to your AWS account using the DeepComposer device.

2. Launch DeepComposer Studio

• Launch Studio: Launch the DeepComposer Studio application on the device.
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AWS DeepComposer Studio launch screen

3. Create a Project

• Create Project: Create a new project in DeepComposer Studio.
• Select Dataset: Choose a dataset from the available options or upload your own.

4. Train Model

• Train Model: Train a generative model using the selected dataset. You can experiment with different hyperparameters to optimize the model's performance.
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AWS DeepComposer Train Model screen

5. Generate Samples

• Generate Samples: Use the trained model to generate new samples.

AWS DeepComposer Generate Samples screen

6. Experiment and Explore

• Experiment: Explore different generative models and techniques.
• Create Custom Models: Create your own custom generative models.

Additional Considerations

• Datasets: Experiment with different datasets to see how they impact the generated samples.
• Hyperparameters: Tune hyperparameters to optimize the performance of your models.
• Visualization: Use the DeepComposer Studio to visualize the generated samples and understand the model's behavior.

AWS Comprehend Medical LAB

AWS Comprehend Medical is a natural language processing (NLP) service specifically designed for healthcare and life sciences applications. It helps you extract insights from medical text, such as clinical notes, medical reports, and patient records.

Prerequisites

• An AWS account
• Basic understanding of AWS services

Step-by-Step Guide

1. Create a Comprehend Medical Job

• Launch Comprehend Medical: In the AWS Management Console, search for "Comprehend Medical" and launch the service.
• Create Job: Click on "Create job".
• Provide Job Details: Enter a name for your job, select the desired feature (e.g., entity recognition, medical terminology extraction), and specify the input data.

AWS Comprehend Medical Create Job screen

2. Submit Data

• Submit Data: Submit the medical text data you want to analyze. You can either upload a file or provide the text directly.

3. Monitor Job

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

AWS Comprehend Medical Monitor Job screen

4. Analyze Results

• Analyze Results: Review the results of your job, which will vary depending on the feature you selected. For example, entity recognition will identify medical entities like medications, diagnoses, and procedures.

Additional Considerations

• Features: Comprehend Medical offers a variety of features, including entity recognition, medical terminology extraction, and relationship extraction.
• Data Format: Ensure your data is in a supported format (e.g., text file, JSON).
• Data Privacy: Be mindful of data privacy regulations when handling medical data.
• Custom Models: Create custom models for more tailored results.

 

AWS Comprehend LAB

 AWS Comprehend is a natural language processing (NLP) service that helps you understand text and extract insights from it. It offers features like sentiment analysis, entity recognition, and topic modeling.

Prerequisites

• An AWS account
• Basic understanding of AWS services

Step-by-Step Guide

1. Create a Comprehend Job

• Launch Comprehend: In the AWS Management Console, search for "Comprehend" and launch the service.
• Create Job: Click on "Create job".
• Provide Job Details: Enter a name for your job, select the desired feature (e.g., sentiment analysis, entity recognition), and specify the input data.

AWS Comprehend Create Job screen

2. Submit Data

• Submit Data: Submit the text data you want to analyze. You can either upload a file or provide the text directly.

3. Monitor Job

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

AWS Comprehend Monitor Job screen

4. Analyze Results

• Analyze Results: Review the results of your job, which will vary depending on the feature you selected. For example, sentiment analysis will provide a sentiment score and label, while entity recognition will identify entities in the text.

Additional Considerations

• Features: Comprehend offers a variety of features, including sentiment analysis, entity recognition, topic modeling, key phrase extraction, and custom labels.
• Data Format: Ensure your data is in a supported format (e.g., text file, JSON).
• Batch Processing: For large datasets, use batch processing to analyze the data more efficiently.
• Custom Models: Create custom models for more tailored results.

AWS CodeGuru LAB

 AWS CodeGuru is a service that helps you identify performance and code quality issues in your Python and Java applications. It uses machine learning to analyze your code and provide actionable recommendations.

Prerequisites

• An AWS account
• Basic understanding of AWS services
• Python or Java application code

Step-by-Step Guide

1. Create a CodeGuru Profiler Group

• Launch CodeGuru Profiler: In the AWS Management Console, search for "CodeGuru Profiler" and launch the service.
• Create Group: Click on "Create group".
• Provide Group Details: Enter a name for your group and select the desired configuration settings (e.g., region, retention period).

AWS CodeGuru Profiler Create Group screen

2. Configure Profiling

• Configure Profiling: Configure your application to enable profiling. This typically involves adding a profiling agent to your application.

3. Start Profiling

• Start Profiling: Start profiling your application to collect performance data.

4. View Profiler Results

• View Results: Use the CodeGuru Profiler console to view the profiling results, including flame graphs, performance metrics, and recommendations.

AWS CodeGuru Profiler View Results screen

5. Create a CodeGuru Reviewer Repository

• Create Repository: Create a CodeGuru Reviewer repository in your AWS account.

AWS CodeGuru Reviewer Create Repository screen

6. Configure Repository

• Configure Repository: Configure your repository to enable CodeGuru Reviewer to analyze your code.

7. Analyze Code

• Analyze Code: CodeGuru Reviewer will analyze your code and provide recommendations for improving performance and code quality.

AWS CodeGuru Reviewer Analyze Code screen

Additional Considerations

• Custom Metrics: Create custom metrics to track specific performance indicators.
• Integration: Integrate CodeGuru with other AWS services like CodeBuild and CodePipeline for continuous integration and delivery.
• Customizations: Customize CodeGuru to meet your specific needs using configuration options and custom rules.

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.