Requirements & Constraints
Define workload needs, assumptions and limits before selecting components.
System Design & Architecture Training · IT Infrastructure, Applications & AI
Design systems around real requirements.
Practical, instructor-led training that connects IT infrastructure, application architecture and AI system design.
Learn to turn business requirements into technical designs, compare alternatives and explain decisions involving scalability, reliability, security, integration, performance and cost.
Choose a focused learning track or combine related areas to understand how infrastructure, applications and AI components work together.
Live Virtual · Classroom · On-Site · Hybrid · Worldwide
Your Trainer
Connect cloud services with infrastructure expertise.
Ahmad Majeed Zahoory is an IT consultant and corporate technology trainer with experience across enterprise infrastructure, cloud architecture, networking and technical enablement.
His training approach helps participants connect requirements with design decisions across infrastructure, applications and AI systems—including scalability, reliability, security, integration, performance and cost.
Sessions combine technical explanation, scenario analysis, architecture exercises and design reviews, helping participants compare alternatives, map dependencies and clearly justify their decisions.
Architecture Beyond Diagrams
Understand why a design works and where it can fail.
An architecture diagram shows components and connections. A useful design also explains capacity assumptions, data flows, security boundaries, failure behavior and operational responsibilities.
Training uses requirements, scenarios and design reviews to help participants evaluate these decisions. The emphasis is on reasoning: why a component is needed, which alternatives exist and what each choice means for the wider system.
Failure & Recovery
Identify dependencies, failure modes and the recovery behavior a design requires.
Operations & Ownership
Clarify how teams will deploy, monitor, troubleshoot and maintain the system.
System Design & Architecture Training Areas
Three tracks. A connected view of the system.
Design perspective
Explore compute, storage, networking, virtualization, identity and hybrid connectivity. Connect workload requirements with capacity, availability, security, backup and disaster recovery decisions.
Practical focus
Produce an infrastructure proposal with a topology, sizing assumptions, dependencies and recovery approach.
Topics can include
- Business and technical requirements
- Workload characteristics and capacity assumptions
- Compute, storage and virtualization choices
- Network topology, segmentation, DNS and load balancing
- Identity services and administrative access
- On-premises, cloud and hybrid architectures
- Redundancy, failure domains and dependency mapping
- Recovery objectives, backup and restoration design
- Monitoring, maintainability and operational ownership
Design perspective
Explore service boundaries, APIs, databases, caching, messaging and integration. Examine how applications behave as traffic grows, dependencies slow down or individual components fail.
Practical focus
Develop an application design with component boundaries, request flows, data choices and an explanation of key trade-offs.
Topics can include
- Functional requirements and quality requirements
- Workload estimation and performance objectives
- Monolithic, modular and microservices approaches
- API design and service-to-service communication
- Database selection, data models and access patterns
- Caching strategies and invalidation
- Queues, events and asynchronous processing
- Consistency, replication and partitioning
- Timeouts, retries, idempotency and failure handling
- Observability, deployment and recovery considerations
Design perspective
Explore how models, enterprise data, retrieval, tools and user interfaces connect. Evaluate the implications of model selection, data access, response quality, latency and cost.
Practical focus
Develop an AI solution architecture with data flows, evaluation criteria, access controls and fallback behavior.
Topics can include
- Defining the AI use case and measurable success criteria
- Model selection and hosted versus self-managed inference
- Data ingestion, preparation and access permissions
- Retrieval-augmented generation (RAG)
- Embeddings, vector search and retrieval design
- Prompt construction and context management
- Tool use, AI agents and approval boundaries
- Evaluation datasets and quality measurement
- Prompt injection, sensitive data and output validation
- Monitoring, human review, latency and inference costs
Shared Architecture Principles
Apply consistent reasoning across all three tracks.
| Design concern | Questions participants learn to ask |
|---|---|
| Requirements | What must the system do, for whom and under which constraints? |
| Capacity | What workload must it support, and which assumptions need validation? |
| Reliability | What happens when a component or dependency fails? |
| Security | Who can access the system, its data and its administrative functions? |
| Performance | Where does time accumulate, and which bottleneck matters most? |
| Operations | How will teams deploy, monitor, troubleshoot and recover the system? |
| Cost | Which resources and usage patterns drive spending? |
| Evolution | How can the system change without unnecessary disruption? |
Scenario-Based Design Workshops
Work from a requirement to a defensible proposal.
Exercises move through requirement analysis, architecture development and review. Participants compare options and explain their choices.
Design an enterprise application
Plan infrastructure around demand, availability, recovery and critical dependencies.
Handle growing workloads
Identify where caching, asynchronous processing, database changes or added capacity can support growth.
Plan a hybrid application
Map connectivity, identity, data flows and ownership across on-premises and cloud components.
Design an internal AI assistant
Connect ingestion, permission-aware retrieval, inference and the user interface. Define evaluation and fallback behavior.
Review failure scenarios
Examine database, network or AI service failures. Define detection, fallback and recovery behavior.
Evaluate security boundaries
Map trust zones, identities, permissions and sensitive data flows. Identify required controls and separation.
Who the Training Is For
Match the learning path to the decisions participants make.
| Audience | Relevant focus |
|---|---|
| Infrastructure and network engineers | Infrastructure architecture, capacity, connectivity and resilience |
| Software developers and technical leads | Application boundaries, APIs, data and distributed-system behavior |
| Cloud and solution architects | Integration, deployment choices and cross-layer trade-offs |
| AI engineers and application teams | AI integration, retrieval, inference and evaluation |
| DevOps, platform and SRE teams | Deployment, observability, reliability and recovery |
| Technology managers | Requirements, architectural review, risk and cost decisions |
Customized Corporate Training
Start with the architecture decisions your team needs to make.
Programs can support a new initiative, modernization project or internal capability-building requirement.
The scope is developed by identifying:
- The systems and responsibilities involved.
- Participants’ existing technical knowledge.
- The decisions they need to make.
- Relevant constraints and failure scenarios.
- Expected exercises and design outputs.
A focused program can cover one track in depth. A combined program can show how the layers interact, with depth agreed for each area.
Flexible Training Delivery
Make time for design, discussion and review.
Programs can take the form of focused workshops, structured corporate training or project-oriented architecture sessions.
Live Virtual Training
Interactive online sessions with architecture walkthroughs, collaborative exercises and guided discussion.
Classroom / On-Site Training
Face-to-face workshops with group design activities, diagram development and technical reviews.
Hybrid Delivery
A combination of remote and classroom sessions arranged around the audience and learning objectives.
Frequently Asked Questions
Architecture training, clarified.
Does every program cover infrastructure, applications and AI?
No. Choose one track or combine selected areas. The scope and depth are agreed before delivery.
Is this primarily interview preparation?
The focus is enterprise design and architectural reasoning. Interview preparation requires a separately agreed scope.
Does the training include coding or deployment?
Design exercises are central. Coding, configuration and deployment activities can be included when relevant to the selected program.
Is the training tied to one cloud provider?
The core approach can remain vendor-neutral. Examples can use selected cloud or on-premises technologies where appropriate.
Can we use our own architecture as a discussion example?
Yes. Sanitized diagrams, requirements and scenarios can be used to make the training relevant to your environment.
Does AI architecture training include building foundation models?
That is a separate specialist scope. This track focuses on designing systems that integrate AI capabilities, data, retrieval, tools and applications.
Will workshop designs be ready for production?
Workshop outputs are learning artifacts. Production use requires validation against actual workloads, security requirements, operational constraints and test results.
Discuss Your Architecture Training Requirements
Help your team make clearer design decisions.
Whether your priority is infrastructure resilience, application scalability or AI integration, define a learning program around the systems your team needs to design and support.
Share your audience, technical environment and learning objectives to select the appropriate track and practical exercises.