Software Architecture with AI Assistance
Description
This hands-on course strengthens architectural thinking and system design skills while incorporating AI-assisted modeling workflows. Participants practice designing structured, scalable systems and learn how AI can support faster exploration of design alternatives, diagram generation, and architectural refinement—without replacing critical engineering judgment. The outcome is clearer architectural reasoning and more confident design decision-making.
Indicative Duration: 48 training hours
*Duration is adjusted based on the final scope and the target audience.
Scope
| 1. Architecture Foundations | • Architecture Fundamentals • Architecture Types (monolith, layered, microservices) • AI Practice: AI explains pros/cons, suggests architectures for examples |
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| 2. Architecture Modeling & Visualization | • UML & C4 diagrams • AI Practice: AI generates diagrams from textual descriptions |
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3. Architectural Patterns
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• Layered and hexagonal architecture patterns • AI Practice: AI scaffolds module separation and boundaries |
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| • Common design patterns (Singleton, Factory, Observer, Strategy) • AI Practice: AI generates examples, refactors code to patterns |
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| 4. Domain Modeling | • Domain-Driven Design basics • AI Practice: AI assists in modeling aggregates and entities |
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5. Distributed and Event-Driven Architectures
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• Event-driven architectures, message brokers, and streaming • AI Practice: AI suggests event flows, async design, topic/channel structure |
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| • Microservices communication patterns (REST, gRPC, async events) • AI Practice: AI scaffolds service contracts and API stubs |
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| 6. Data Architecture | • Persistence patterns: RDBMS, NoSQL, CQRS, • Event Sourcing • AI Practice: AI suggests DB layouts and query patterns |
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| 7. Cloud-Native Architecture | • Cloud-native architecture principles (12-factor apps, resilience, scaling) • AI Practice: AI reviews system design, highlights bottlenecks |
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8. Observability, Security, and Reliability
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• Security • Observability • Monitoring • AI Practice: AI scaffolds security layers, metrics, logging |
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| • Performance and reliability considerations • AI Practice: AI proposes caching, load balancing, failover patterns |
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| 9. Capstone Project | • End-to-end architecture design • AI Practice: AI assists in generating full diagrams, service breakdowns, and scaffolded code |
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Learning Objectives
Upon completion of the course participants will be able to:
- Analyze and compare architectural styles based on system requirements and constraints
- Design structured system architectures using established patterns and modeling techniques
- Apply architectural principles to scalability, resilience, security, and maintainability challenges
- Use AI tools to generate, refine, and critique architectural designs and documentation
- Produce a complete high-level system design with clear service boundaries and interaction flows
Target Audience
- Roles: Software Engineers, Backend Developers, Solution Architects, Technical Leads
- Seniority: Mid-Level to Senior Professionals
Prerequisite Knowledge
- Solid programming experience in at least one language
- Basic understanding of backend application development
- Familiarity with REST-based systems
Delivery Method
Sessions can be delivered via the following formats:
- Live Online – Interactive virtual sessions via video conferencing
- On-Site – At your organization’s premises
- In-Person – At Code.Hub’s training center
- Hybrid – A combination of online and in-person sessions
