AWS App Runner LAB

 AWS App Runner is a fully managed service that makes it easy to deploy and scale web applications. It takes care of all the infrastructure, so you can focus on building your applications.

Prerequisites

• An AWS account
• A source code repository (e.g., GitHub, GitLab)
• A Dockerfile or build specification

Step-by-Step Guide

1. Create a Service

• Launch App Runner: In the AWS Management Console, search for "App Runner" and launch the service.
• Create Service: Click on "Create service".
• Provide Service Details: Enter a name for your service and select your source code repository.

AWS App Runner Create Service screen

2. Configure Service

• Configure Service: Specify the build and deployment settings for your service, including the Dockerfile or build specification, environment variables, and scaling configurations.

AWS App Runner Configure Service screen

3. Create Service

• Create Service: Click on "Create service" to deploy your application.

4. View Service Details

• View Details: Once your service is deployed, you can view its details, including the URL, status, and metrics.

AWS App Runner View Service Details screen

5. Access Application

• Access Application: Use the provided URL to access your deployed application.

Additional Considerations

• Deployment Strategies: Choose from different deployment strategies like Canary deployments or Blue/Green deployments.
• Scaling: App Runner automatically scales your application based on traffic.
• Custom Domains: Use custom domains to map your application to a specific domain name.
• Integration: Integrate App Runner with other AWS services like CodePipeline for continuous delivery.

AWS Fraud Detector LAB

 

AWS Fraud Detector is a managed service that helps you build, train, and deploy machine learning models to detect fraud in your applications. It provides pre-built machine learning models and tools to help you identify fraudulent activity.

Prerequisites

• An AWS account
• Basic understanding of AWS services

Step-by-Step Guide

1. Create a Detector

• Launch Fraud Detector: In the AWS Management Console, search for "Fraud Detector" and launch the service.
• Create Detector: Click on "Create detector".
• Provide Detector Details: Enter a name for your detector and select the desired configuration settings (e.g., region, data privacy).

AWS Fraud Detector Create Detector screen

2. Create Labels

• Create Labels: Create labels to represent fraudulent and legitimate events in your data.

AWS Fraud Detector Create Labels screen

3. Ingest Data

• Ingest Data: Ingest your historical data into Fraud Detector. You can either upload a file or use a data source like Kinesis Data Streams.

AWS Fraud Detector Ingest Data screen

4. Train Model

• Train Model: Train a machine learning model using your labeled data. Fraud Detector provides pre-built models or you can create your own.

AWS Fraud Detector Train Model screen

5. Create a Detector Version

• Create Version: Create a version of your detector to deploy.

6. Deploy Detector

• Deploy Detector: Deploy your detector to an endpoint.

AWS Fraud Detector Deploy Detector screen

7. Use Detector

• Use Detector: Send real-time events to the detector endpoint to receive fraud predictions.

Python

import boto3

# Create a Fraud Detector client
client = boto3.client('frauddetector')

# Send an event to the detector
response = client.detect_events(
    DetectorId='your-detector-id',
    Events=[
        {
            'EventId': 'event-1',
            'EventType': 'transaction',
            # Other event data
        }
    ]
)

Additional Considerations

• Pre-built Models: Use pre-built models for common fraud scenarios.
• Custom Models: Create custom models for more tailored fraud detection.
• Integration: Integrate Fraud Detector with other AWS services like Lambda and Kinesis.
• Monitoring: Monitor the performance of your detector and make adjustments as needed.

AWS DevOps Guru LAB

 AWS DevOps Guru is a service that proactively identifies anomalies in your applications. It uses machine learning to analyze your application metrics and logs to detect potential issues.

Prerequisites

• An AWS account
• Running applications on AWS

Step-by-Step Guide

1. Enable DevOps Guru

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

AWS DevOps Guru Enable screen

2. Configure Resources

• Configure Resources: Specify the AWS resources you want DevOps Guru to analyze.

AWS DevOps Guru Configure Resources screen

3. View Anomalies

• View Anomalies: DevOps Guru will analyze your resources and identify any anomalies. You can view these anomalies in the console.

AWS DevOps Guru View Anomalies screen

4. Investigate Anomalies

• Investigate Anomalies: Click on an anomaly to view more details, including the root cause and potential solutions.

AWS DevOps Guru Investigate Anomalies screen

5. Take Action

• Take Action: Based on the insights provided by DevOps Guru, take the necessary steps to address the identified anomalies.

Additional Considerations

• Custom Metrics: Provide custom metrics to DevOps Guru for more accurate anomaly detection.
• Integration: Integrate DevOps Guru with other AWS services like CloudWatch and X-Ray for comprehensive monitoring and analysis.
• Automation: Automate response actions using AWS Lambda and other services.

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.
Opens in a new window www.cloudbrigade.com
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.
Opens in a new window aws.amazon.com
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.