Cloud Solutions Architect and Platform Engineering

Getting Started

• Goal-setting session by Program Manager
• Orientation session
• Prerequisites overview and getting started guide

Course Requirements

• Essential Knowledge:
• Basic Linux commands (ls, cd, mkdir, etc.)
• Fundamental programming skills (ability to write a "Hello World" application in any language)

DevOps and Cloud Use Cases

• 1. Enterprise System Management
• Configure and maintain Linux systems to optimize performance and automate admin tasks.

• 2. Network Setup and Optimization
• Manage complex network configurations to ensure secure, efficient connectivity.

• 3. Deployment Troubleshooting
• Resolve deployment issues quickly to minimize downtime and ensure smooth rollouts.

• 4. Proactive Network Troubleshooting
• Diagnose and fix network issues to boost server and application performance.

• 5. Task Automation
• Automate repetitive tasks to increase efficiency and reduce manual intervention.

• 6. System Maintenance and Monitoring
• Automate system checks and maintenance for consistent performance.

• 7. CI/CD Integration
• Augment modern GitOps workflows by using shell scripts for task automation, environment setup, and debugging within CI/CD pipelines.

Compute Services

• EC2 - Elastic Compute Cloud
• Lambda - Serverless Functions
• Fargate - Serverless Containers

Load Balancing

• Application Load Balancer (ALB)
• Network Load Balancer (NLB)

Networking

• VPC - Virtual Private Cloud
• Amazon CloudFront - Content Delivery Network
• Route53 - DNS Service

Storage

• Amazon EBS and EFS - Block and File Storage
• Amazon S3 - Object Storage

Database Services

• RDS - Relational Database Service
• DynamoDB - NoSQL Database
• Aurora - High-Performance Database
• Redshift - Data Warehouse

Security Services

• Identity and Access Management (IAM)
• Secrets Manager
• Key Management Service (KMS)
• Certificate Manager (ACM)

Monitoring and Logging

• Amazon CloudWatch
• AWS CloudTrail
• Application Integration
• Amazon SQS - Standard/FIFO queues
• Amazon SNS - Pub/Sub messaging
• Amazon EventBridge - Event-driven architecture
• AWS Step Functions - Workflow orchestration

Analytics & Data Processing

• Amazon Athena - S3 data querying
• Amazon Kinesis - Real-time streaming
• AWS Glue - ETL services
• Amazon EMR - Big data proce

Compute and Load Balancing

• Design resilient compute architectures using EC2, Lambda, and Fargate. Implement Elastic Load Balancing for high availability across multiple Availability Zones.

Networking

• Architect secure VPC environments with CloudFront for global content delivery and Route 53 for DNS resolution and health checks.

Storage

• Select appropriate storage services based on access patterns. Implement EBS for block storage, EFS for file systems, and S3 with lifecycle policies.

Database

• Design database solutions using RDS Multi-AZ, DynamoDB for NoSQL, Aurora for enterprise applications, and Redshift for analytics.

Security

• Implement IAM with least privilege, configure encryption using KMS, manage secrets with Secrets Manager, and deploy certificates through ACM.

Monitoring and Logging

• Establish monitoring using CloudWatch metrics and alarms. Implement audit trails with CloudTrail for compliance and governance.

Application Integration

• Design loosely coupled architectures using SQS, SNS, EventBridge, and Step Functions for workflow orchestration.

Analytics & Data Processing

• Process data using Athena for querying, Kinesis for streaming, Glue for ETL, and EMR for big data processing.

AWS SAA-C3 Exam Readiness

• This curriculum prepares you for the AWS Certified Solutions Architect Associate (SAA-C03) exam through hands-on practice with real AWS services and real-world scenarios. You'll master AWS terminology, service patterns, and architectural decision-making skills that appear on the certification exam, building both exam confidence and practical cloud architecture expertise.

• Introduction to Claude Code & AI Coding Assistants
• Setting Up Claude Code in Your Dev Environment
• Claude Code for Shell Script & Bash Generation
• AI-Assisted Python Scripting for Automation
• Prompt Engineering Best Practices for DevOps
• Claude Code Chat for Code Explanation & Debugging
• AI Code Review & Quality Improvement
• Claude Code for Code Refactoring & Documentation
• Ethical AI Usage & Best Practices

• Setting up and configuring Claude Code

• Leveraging AI for shell script and Python script generation

• Using Claude Code for infrastructure code generation

• Applying prompt engineering techniques for DevOps

• Accelerating shell and Python development with AI assistance throughout the course

• IaC Fundamentals: Terraform, State Management, and Backends.
• Enterprise Architecture: Modules, Code Organization, and Advanced IaC Patterns & Anti-Patterns.
• The IaC Ecosystem: Cost Estimation (Infracost), Infrastructure Testing (Terratest), and Policy as Code (Sentinel).
• Landscape Comparison: Multi-Cloud Infrastructure management, evaluating Pulumi, AWS CDK, and Azure Bicep.
• Enterprise CI/CD Integration: Automating deployments through pipelines(GitOps‑driven workflows) to eliminate manual resource creation.

• Infrastructure Provisioning

• Design and deploy scalable cloud infrastructure using declarative syntax for consistent, repeatable resource creation.

• State Management

• Implement remote backends, state locking, and collaborative workflows for safe team-based infrastructure changes.

• Modular Architecture

• Create reusable modules for common infrastructure patterns, promoting standardization across enterprise deployments.

• Advanced Resource Management

• Handle complex dependencies, conditional logic, and lifecycle management with data sources and dynamic blocks.

• CI/CD Integration

• Integrate Terraform into automated pipelines using GitOps practices with plan validation and controlled deployments.

• Security and Compliance

• Implement infrastructure security best practices and manage sensitive data through policy-as-code frameworks.

• Enterprise Scaling

• Design architectures for large-scale environments with workspace management and governance frameworks.

DevSecOps Philosophy and Shift-Left Security

AI-Assisted Security Automation

• Secure SDLC Activities and Security Gates
• Security Requirements (Requirements)
• Threat Modelling (Design)
• Static Analysis and Secure by Default (Implementation)
• Dynamic Analysis(Testing)
• OS Hardening, Web/Application Hardening (Deploy)
• Security Monitoring/Compliance (Maintain)

DevSecOps Maturity Model (DSOMM)

• How to go from Maturity Level 1 to Maturity Level 4

Pentesting and Vulnerability Assessment

Software Component Analysis (SCA) in CI/CD pipeline

• Embedding SCA tools like OWASP Dependency Checker, Safety, RetireJs and NPM Audit, Snyk into the pipeline.

SAST (Static Analysis) in CI/CD pipeline

• Embedding SAST tools like Find Bugs into the pipeline.

DAST (Dynamic Analysis) in CI/CD pipeline

Vulnerability Management with custom tools

• Embed security across the entire SDLC — from threat modelling at design through compliance monitoring in production, so security is never a last-minute gate.
• Build a fully automated security pipeline — integrate SAST, DAST, and SCA tooling so vulnerabilities are caught continuously, not manually.
• Scan and harden your infrastructure — apply CIS Benchmarks, IaC scanning, and container image analysis before anything reaches production.
• Enforce policy and compliance automatically — use OPA and Kyverno so governance is code, not a checklist.
• Manage secrets and sensitive data at enterprise scale — implement Vault and AWS Secrets Manager so credentials never live in code or pipelines.
• Assess and advance your organization's security maturity — use DSOMM to diagnose where you are and build a concrete roadmap to Level 4.
• Use AI to accelerate security work — generate automation scripts, triage findings faster, and augment your security analysis without deep coding expertise.

Container Technologies

• Docker - Containerization Platform
• Kubernetes (EKS) - Container Orchestration

GitOps and Deployment

• ArgoCD - GitOps Continuous Delivery
• Flux - GitOps Toolkit

Monitoring and Observability

• Grafana - Visualization Platform
• Prometheus - Metrics and Monitoring

Kubernetes Ecosystem

• Karpenter - Node Autoscaling
• Helm - Package Manager
• Service Mesh (Istio/Linkerd)
• Kubernetes Operators (MongoDB, Kafka)

Storage Solutions

• Longhorn - Distributed Storage
• Portworx - Enterprise Storage

Networking

• Calico - Network Security
• Cilium - eBPF-based Networking

Security and Management

• Kubernetes Security
• Rancher - Multi-cluster Management

Logging and Cost Optimization

• ELK Stack - Elasticsearch, Logstash, Kibana
• Cost Optimization Strategies

• 1. Enterprise Container Strategy: Make informed decisions between managed vs self-hosted solutions, understanding cost-benefit trade-offs for enterprise-scale deployments.
• 2. GitOps Tool Selection: Compare leading GitOps platforms and select the right solution based on organizational requirements and team capabilities.
• 3. Production Monitoring Architecture: Design scalable observability solutions that provide actionable insights for enterprise workloads and complex distributed systems.
• 4. Enterprise Autoscaling Decisions: Evaluate Karpenter's node provisioning against Cluster Autoscaler limitations, implementing cost-effective scaling for enterprise workloads with mixed instance types and spot instances.
• 5. Service Mesh Evaluation: Choose appropriate service mesh solutions based on security requirements, performance needs, and operational complexity considerations.
• 6. Enterprise Storage Architecture: Evaluate cloud-native storage solutions for persistent workloads, comparing distributed storage platforms against cloud provider storage classes for data resilience and performance.
• 7. Stateful Workload Management: Assess when to use Kubernetes operators versus managed cloud services for critical enterprise data systems and applications.
• 8. Enterprise Networking Design: Select and configure networking solutions that meet enterprise security, compliance, and performance standards.
• 9. Multi-Cluster Governance: Implement centralized management and cost optimization strategies across distributed Kubernetes environments.

• SRE Foundations
• Measuring Reliability
• Observability
• Incident Management
• Chaos Engineering
• Chaos in CI/CD

• Reliability Engineering:

• Define and manage SLIs, SLOs, and error budgets to align engineering decisions with business expectations.

• Observability

• Build observable systems that detect and diagnose failures before users do.

• Incident Management

• Run structured incident response and drive continuous reliability improvements through blameless post-mortems.

• Foundations
• MLOps
• The ML Lifecycle — from problem framing to production monitoring

• MLOps

• Data versioning and pipeline automation — MLflow, DVC
  Model training, validation, and registry
  CI/CD for ML pipelines
  Model monitoring and drift detectionAWS SageMaker

• LLMOps

• Foundation model management
  Prompt engineering and management
  RAG architectures and vector databases
  Fine-tuning and model adaptation
  LLM System Design — Architecture and Scaling
  LLM System Design — Optimization and Deployment Strategies
  Safety, governance, and cost optimization
  AWS Bedrock

• Agentic AI Operations

• Task planning and reasoning systems
  Tool integration and orchestration
  Memory systems and context management
  Multi-agent coordination
  Safety guardrails and feedback loops