Instructor-led 4-days

Course Description:

In this 4-day, hands-on course, participants will explore the power of integrating AI into Amazon Web Services (AWS) cloud infrastructure to automate configurations, enhance monitoring, improve security, and simplify troubleshooting. Through practical exercises and real-world scenarios, attendees will gain skills using AWS-specific AI tools, such as Amazon SageMaker, AWS CloudFormation, AWS CloudWatch, and AWS Security Hub. By the end of the course, participants will understand how to leverage AI to optimize performance, improve resource management, and proactively manage issues in an AWS environment, equipping them to deploy scalable, efficient, and secure cloud infrastructures.

Learning Objectives:

1. Understand the core components of AWS cloud infrastructure and how to use AI for optimizing management and operations.
2. Configure AWS environments using AI-driven tools for automated setup and performance optimization.
3. Build scalable and resilient architectures in AWS that leverage AI to improve resource allocation and cost efficiency.
4. Manage and monitor AWS environments with AI, enabling proactive problem detection and mitigation.
5. Troubleshoot AWS cloud issues using AI tools for predictive analysis and diagnostics.
6. Apply best practices for security and compliance when deploying AI in AWS environments.
7. Demonstrate the ability to implement and manage AI-driven solutions to support high-performance, cost-effective AWS cloud infrastructures.

Part 1: Introduction to AWS Cloud and AI Integration

Module 1: Foundations of AWS Cloud Infrastructure

• Topics Covered:
  • Overview of AWS services and cloud models (IaaS, PaaS, SaaS)
  • Key components: Amazon EC2, S3, VPC, RDS, Lambda, and DynamoDB
  • Navigating AWS Management Console, CLI, and AWS SDKs
• Hands-on Labs:
  • Setting up an AWS account and navigating the AWS Management Console
  • Basic provisioning of resources (EC2, S3, VPC)

Module 2: AI in AWS Cloud Infrastructure

• Topics Covered:
  • Introduction to AI’s role in cloud infrastructure: benefits, tools, and applications
  • Overview of AI-driven AWS tools (Amazon SageMaker, Comprehend, Rekognition)
  • Ethical and compliance considerations for AI in AWS environments
• Hands-on Labs:
  • Exploring AI services within AWS (SageMaker, Comprehend, Rekognition)
  • Identifying use cases for AI-enhanced cloud management on AWS

Part 2: Configuring and Building AWS Cloud Infrastructure Using AI

Module 3: AI-Driven AWS Configuration

• Topics Covered:
  • Using AWS CloudFormation for infrastructure as code and automating configurations
  • AI-powered configuration management with SageMaker and automation tools
  • Best practices for automated setup and optimal configurations using AI
• Hands-on Labs:
  • Building infrastructure using AWS CloudFormation templates and enhancing configurations with AI insights
  • Using SageMaker for analyzing configuration data and recommending optimizations

Module 4: Building Scalable and Optimized Architectures in AWS

• Topics Covered:
  • Designing scalable architectures using AWS auto-scaling, Elastic Load Balancing, and RDS
  • Integrating AI to predict workload patterns and optimize resource allocation
  • Using AI to set up and manage AWS cost-optimization practices
• Hands-on Labs:
  • Configuring auto-scaling and load balancing using AI-driven insights from SageMaker and CloudWatch
  • Applying machine learning models to forecast workload demands and configure scaling for cost efficiency

Part 3: Managing and Monitoring AWS Cloud Infrastructure Using AI

Module 5: AI-Enhanced AWS Management and Monitoring

• Topics Covered:
  • Monitoring AWS resources with CloudWatch, CloudTrail, and AI-powered observability tools
  • Real-time anomaly detection using Amazon Lookout for Metrics and SageMaker
  • Setting up automated alerts and actions with CloudWatch Alarms and AI-driven insights
• Hands-on Labs:
  • Configuring CloudWatch dashboards, log analytics, and setting up AI-based alerts
  • Using Amazon Lookout for Metrics to detect anomalies in resource usage and performance

Module 6: Security and Compliance in AI-Driven AWS Cloud Management

• Topics Covered:
  • AWS security fundamentals, compliance standards, and integrating AI for enhanced security
  • Using AWS Security Hub, Amazon Macie, and GuardDuty for AI-based security management
  • Implementing AI-driven intrusion detection and automatic response strategies
• Hands-on Labs:
  • Configuring AWS Security Hub and GuardDuty for automated security scans and compliance checks
  • Setting up AI-driven alerts and remediation workflows for threat detection and response

Part 4: Troubleshooting and Optimizing AWS Cloud Infrastructure with AI

Module 7: Troubleshooting AWS with AI-Powered Tools

• Topics Covered:
  • Diagnosing common AWS issues using AI-enhanced diagnostics and root cause analysis
  • Leveraging predictive analytics with SageMaker for proactive troubleshooting
  • Using AI to resolve performance bottlenecks and improve latency and reliability
• Hands-on Labs:
  • Configuring an AI-driven troubleshooting workflow using SageMaker and Lookout for Vision
  • Practicing root cause analysis and predictive troubleshooting exercises on AWS resources

Module 8: Capstone Project and Case Studies

• Topics Covered:
  • Review of case studies where AI improved AWS cloud operations and management
  • Capstone project: Design, configure, manage, and troubleshoot an AI-enhanced AWS environment to meet business needs
• Hands-on Labs:
  • Capstone project: Comprehensive application of learned skills to build and manage a complex AWS environment
  • Peer review and feedback, followed by instructor feedback on implementation