Instructor-led 4-days

Course Description:

This 4-Part hands-on course provides an in-depth exploration of using AI to enhance Google Cloud Platform (GCP) cloud infrastructure management. Through practical exercises, case studies, and guided scenarios, participants will learn how to use GCP’s AI tools and services, such as Google AI Platform, Cloud AutoML, and Cloud Operations, to automate configurations, optimize resources, strengthen security, and streamline troubleshooting. By the end of the course, attendees will have the skills to deploy, manage, and troubleshoot scalable, cost-effective, and secure GCP infrastructures powered by AI, making them more effective cloud engineers and architects.

Learning Objectives:

1. Understand core GCP infrastructure components and how AI enhances management and optimization.
2. Configure GCP environments using AI-driven tools to automate setup and improve configurations.
3. Build scalable and resilient cloud architectures on GCP with AI support for optimal resource use and cost efficiency.
4. Manage and monitor GCP environments with AI, enabling proactive issue detection and resource optimization.
5. Troubleshoot GCP infrastructure challenges using AI for diagnostics and predictive analysis.
6. Apply security best practices for deploying AI within GCP environments to ensure compliance and mitigate risks.
7. Demonstrate proficiency in leveraging AI-driven solutions to support high-performance, cost-effective GCP cloud infrastructures.

Part 1: Introduction to GCP and AI Integration

Module 1: Foundations of GCP Cloud Infrastructure

• Topics Covered:
  • Overview of GCP services and cloud models (IaaS, PaaS, SaaS)
  • Core GCP components: Compute Engine, Cloud Storage, VPC, Cloud SQL, and App Engine
  • Familiarization with GCP Console, Cloud Shell, and Resource Manager
• Hands-On Labs:
  • Setting up a GCP project and exploring the Google Cloud Console
  • Creating and managing basic resources like VMs, storage, and networking in GCP

Module 2: AI in GCP Cloud Infrastructure

• Topics Covered:
  • Introduction to AI’s role in cloud infrastructure: benefits, tools, and capabilities
  • Overview of AI-driven GCP tools (Google AI Platform, Cloud AutoML, BigQuery ML)
  • Ethical considerations and compliance in AI for cloud environments
• Hands-On Labs:
  • Exploring AI services within GCP (Google AI Platform, AutoML, Vision AI)
  • Identifying use cases for AI-enhanced management on GCP

Part 2: Configuring and Building GCP Cloud Infrastructure Using AI

Module 3: AI-Driven GCP Configuration

• Topics Covered:
  • Using Google Cloud Deployment Manager for infrastructure as code
  • Automating configurations with AI, using Google AI Platform and machine learning models
  • Best practices for optimal configuration and automation in GCP
• Hands-On Labs:
  • Building infrastructure using Deployment Manager and enhancing configuration with AI models
  • Setting up automated configurations using Cloud Functions with AI triggers

Module 4: Building Scalable and Optimized Architectures on GCP

• Topics Covered:
  • Designing scalable GCP architectures using auto-scaling, Load Balancing, and Cloud Spanner
  • Using AI for predicting workload demands and allocating resources efficiently
  • Applying AI for cost optimization and resource monitoring
• Hands-On Labs:
  • Setting up auto-scaling and load balancing with insights from BigQuery ML and AI models
  • Configuring machine learning models to forecast workload for dynamic scaling and cost management

Part 3: Managing and Monitoring GCP Cloud Infrastructure Using AI

Module 5: AI-Enhanced Management and Monitoring with GCP Tools

• Topics Covered:
  • Monitoring with Cloud Monitoring (formerly Stackdriver), Cloud Logging, and AI-powered observability
  • Real-time anomaly detection using Google AI Platform and BigQuery ML
  • Setting up automated alerts and responses for resource management and performance issues
• Hands-On Labs:
  • Configuring Cloud Monitoring and Cloud Logging dashboards with AI-driven alerts
  • Using BigQuery ML for anomaly detection in monitoring data and creating custom alerts

Module 6: Security and Compliance in AI-Driven GCP Management

• Topics Covered:
  • GCP security fundamentals, compliance standards, and AI-driven security tools
  • Implementing Google Cloud Security Command Center and using AI for security insights
  • Using AI for threat detection and automatic responses
• Hands-On Labs:
  • Configuring Security Command Center and Cloud Armor for AI-powered security monitoring
  • Using AI to automate security compliance checks and implement threat detection workflows

Part 4: Troubleshooting and Optimizing GCP Cloud Infrastructure with AI

Module 7: Troubleshooting GCP Environments with AI-Powered Tools

• Topics Covered:
  • Diagnosing common GCP issues using AI-enhanced diagnostics and root cause analysis
  • Predictive analytics with AI for proactive troubleshooting and performance optimization
  • Using AI to resolve performance bottlenecks and latency issues in GCP environments
• Hands-On Labs:
  • Setting up an AI-driven troubleshooting workflow using BigQuery ML and Cloud Monitoring
  • Practicing root cause analysis and troubleshooting exercises with simulated issues