Configure, Build, Manage, and Troubleshoot GCP Cloud Infrastructure Using AI

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