Architecture decides reliability
Model quality alone is not enough. Serving, caching, and fallback logic determine user experience.
BUNKROS AI Training
Master model families, deployment patterns, and lifecycle operations.
Understand how foundation models are built, selected, hosted, monitored, and upgraded in real production environments.
Why This Matters
Strategic relevance before tactical execution.
Lifecycle complexity grows fast
Without clear versioning and evaluation workflows, model upgrades create regression risk.
Infrastructure costs compound
Efficient deployment patterns can dramatically reduce cost while preserving quality.
What You Will Learn
Practical capabilities you can apply immediately.
- Differentiate model families by architecture, capability, and tradeoffs.
- Design routing patterns across open and closed model providers.
- Set model versioning, evaluation, and rollback practices.
- Implement observability for latency, drift, and quality degradation.
- Plan hybrid deployment with API and self-hosted components.
- Build lifecycle governance for model updates and procurement.
Curriculum Modules
A structured path from foundations to implementation.
Module 1: Foundation Model Landscape
Map major model families and their practical strengths.
Module 2: Inference Architecture
Design robust serving paths, retries, and fallback routing.
Module 3: Performance and Cost Engineering
Optimize throughput, context, and token usage patterns.
Module 4: Evaluation and Version Control
Create guardrails for safe model upgrades and regressions.
Module 5: Monitoring and Drift Management
Track quality, behavior shifts, and incident thresholds.
Module 6: Platform Strategy
Select build-versus-buy paths for your long-term AI stack.
30-Minute Training
One focused sprint to move from theory to repeatable execution.
00:00 - 05:00
Introduction
Define the problem this track solves, pick one real workflow, and set a measurable target for the session.
05:00 - 11:00
Theory Block 1
Map the core principles so your decisions are based on system behavior, not trial-and-error prompting.
11:00 - 17:00
Exercise Block 1
Run a controlled build task with explicit constraints, then measure output quality against your rubric.
17:00 - 23:00
Theory Block 2
Add governance, validation, and failure modes so the workflow remains usable in production.
23:00 - 30:00
Exercise Block 2 + Check
Refine your first build, run a quick knowledge check, and prepare your next learning sprint.
Theory Blocks
Foundations that keep your outputs reliable.
Architecture decides reliability
Model quality alone is not enough. Serving, caching, and fallback logic determine user experience.
Lifecycle complexity grows fast
Without clear versioning and evaluation workflows, model upgrades create regression risk.
Infrastructure costs compound
Efficient deployment patterns can dramatically reduce cost while preserving quality.
Hands-On Exercises
Short builds designed for immediate skill transfer.
Exercise 1: Module 1: Foundation Model Landscape
Map major model families and their practical strengths.
Build a focused workflow step in 6 minutes. Force explicit inputs, expected outputs, and review criteria.
Deliverable: one reusable prompt or SOP with acceptance criteria and one risk note.
Exercise 2: Module 2: Inference Architecture
Design robust serving paths, retries, and fallback routing.
Build a focused workflow step in 6 minutes. Force explicit inputs, expected outputs, and review criteria.
Deliverable: one reusable prompt or SOP with acceptance criteria and one risk note.
Exercise 3: Module 3: Performance and Cost Engineering
Optimize throughput, context, and token usage patterns.
Build a focused workflow step in 6 minutes. Force explicit inputs, expected outputs, and review criteria.
Deliverable: one reusable prompt or SOP with acceptance criteria and one risk note.
Knowledge Check
Validate comprehension before scaling the workflow.
Open Resources
Continue learning with high-quality public material.
Glossary
Key terms you should be fluent in for this track.
Workflow Constraint
A rule that limits ambiguity and keeps output behavior stable across runs.
Quality Gate
A mandatory review checkpoint before downstream use or publication.
Tools Covered
Tooling choices tied to workflow outcomes.
OpenAI
Anthropic
Google Vertex AI
Hugging Face
vLLM
Docker
Kubernetes
Grafana
Langfuse
Who This Is For
Built for operators, builders, and strategic teams.
- Engineers and architects building AI-powered systems.
- Technical product leads shaping model platform strategy.
- AI teams moving from prototype to production scale.
- Consultants designing AI stack decisions for clients.
Outcomes and Career Impact
Execution outcomes with direct professional value.
Outcome
Produce an end-to-end model architecture blueprint.
Outcome
Reduce runtime risk with clear rollback and fallback patterns.
Outcome
Implement model monitoring that catches drift early.
Outcome
Create a practical roadmap for AI infrastructure scaling.
Signals from Practice
Operator-level feedback and implementation sentiment.
"Exactly the architecture depth we needed for production AI."
"This turned model operations into a disciplined engineering function."
Access Models
Free, cohort, and enterprise pathways.
Starter
EUR 0
Architecture checklist and platform comparison matrix.
Pro Cohort
EUR 549
5-week technical lab with architecture feedback.
Enterprise
Custom
AI platform design and implementation advisory.
Ready to Start
Design an AI model stack that survives production reality.
Work through your architecture choices with structured technical review.
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