A tailored course, built for your situation
Scaling LLM Systems from Proof-of-Concept to Production
A structured path to deploy resilient, cost-optimized AI systems at scale
$199 one-time
24-hour access provisioning
30-day money-back guarantee
Hand-built implementation playbook
12 modules. 12 chapters per module. 144 chapters total.
12 modules, each with 12 chapters (144 chapters total), text-based, plus downloadable templates and a hand-built implementation playbook delivered alongside course access.
Most LLM prototypes fail in production , not because of the model, but because of infrastructure, observability, and scaling gaps.
The situation this course is for
You've seen it: a prototype works flawlessly in isolation but collapses under real load. Latency spikes, costs balloon, and reliability breaks. Teams scramble with duct-tape fixes, but the system remains fragile. The jump from PoC to production isn’t about better models , it’s about better engineering, monitoring, and operational discipline. Without a proven framework, even strong teams waste cycles reinventing solutions to known problems.
Who this is for
Technical lead or AI engineer shipping production-grade LLM systems, managing tradeoffs between cost, latency, and reliability.
Who this is not for
This is not for researchers focused on model architecture or data scientists building isolated notebooks. It’s for those responsible for systems that must run 24/7 with real user impact.
What you walk away with
- Deploy LLM systems with predictable cost and performance
- Architect for observability, scaling, and failure recovery
- Optimize inference pipelines for real-world traffic
- Implement monitoring and alerting tailored to AI workloads
- Avoid common anti-patterns in production LLM deployment
The 12 modules (with all 144 chapters)
Module 1. From PoC to Production Mindset
Shift from experimental to operational thinking. Understand the core differences in goals, constraints, and success metrics between prototype and production systems.
12 chapters in this module
- Prototype vs production goals
- Defining operational KPIs
- Cost as a first-class constraint
- Latency budgeting principles
- Error tolerance frameworks
- Team structure for scale
- Tech debt in AI systems
- Versioning models and data
- Rollback readiness
- Staging environments design
- Load testing philosophy
- Production readiness checklist
Module 2. Model Serving Architectures
Compare serving patterns including serverless, batch, real-time, and hybrid. Evaluate tradeoffs in cost, cold starts, and scalability.
12 chapters in this module
- Serverless tradeoffs
- Batch processing pipelines
- Real-time inference design
- Hybrid serving patterns
- Cold start mitigation
- GPU vs CPU allocation
- Model parallelism
- Multi-model routing
- A/B testing infrastructure
- Canary rollout patterns
- Blue-green for models
- Traffic shaping rules
Module 3. Cost Optimization Strategies
Reduce inference costs without sacrificing quality. Apply proven techniques in quantization, distillation, caching, and dynamic batching.
12 chapters in this module
- Quantization techniques
- Model distillation
- Caching response patterns
- Dynamic batching logic
- Spot instance usage
- Auto-scaling thresholds
- Model pruning methods
- Efficient attention patterns
- Token savings tactics
- Prompt compression
- Batch size tuning
- Cost monitoring setup
Module 4. Observability for AI Systems
Implement monitoring that detects drift, latency spikes, and quality decay. Build dashboards that reflect real user impact.
12 chapters in this module
- Latency tracking
- Error rate monitoring
- Token usage trends
- Model drift detection
- Input quality checks
- Output consistency tests
- User feedback loops
- Alerting thresholds
- Log aggregation setup
- Traceability design
- Failure mode logging
- Health check automation
Module 5. Failure Recovery and Resilience
Design systems that degrade gracefully. Implement retry logic, fallbacks, and circuit breakers tailored to AI workloads.
12 chapters in this module
- Retry logic design
- Circuit breaker patterns
- Fallback response strategies
- Graceful degradation
- Rate limiting setup
- Queue management
- Dead letter handling
- Timeout configuration
- Dependency isolation
- State recovery methods
- Idempotency enforcement
- Chaos testing
Module 6. Security and Access Control
Secure model endpoints, manage keys, and control access. Prevent abuse and data leakage in production environments.
12 chapters in this module
- API key management
- Rate limiting rules
- Input sanitization
- Prompt injection defense
- Data leakage prevention
- Role-based access
- Audit logging
- Model watermarking
- Abuse detection
- IP allowlisting
- Secrets rotation
- Zero-trust principles
Module 7. CI/CD for Machine Learning
Automate testing, validation, and deployment of models. Implement pipelines that ensure quality and safety before release.
12 chapters in this module
- Model testing suite
- Data validation checks
- Automated rollback
- Pipeline triggers
- Model registry setup
- Version comparison
- Quality gates
- Canary metrics
- Rollback automation
- Model signing
- Pipeline observability
- Approval workflows
Module 8. Scaling with Traffic Growth
Plan for exponential user growth. Optimize infrastructure to handle spikes without cost overruns or downtime.
12 chapters in this module
- Load forecasting
- Auto-scaling rules
- Regional deployment
- Multi-cloud setup
- Traffic shaping
- Caching layers
- CDN for AI responses
- Database scaling
- Queue buffering
- Concurrency limits
- Peak handling
- Capacity planning
Module 9. Monitoring Model Quality
Track output quality over time. Detect degradation, bias shifts, and edge case failures before users do.
12 chapters in this module
- Output consistency
- Bias detection
- Edge case tracking
- Human-in-the-loop
- Quality scoring
- Feedback annotation
- Drift detection
- A/B quality testing
- Error clustering
- Root cause analysis
- Model retraining
- Quality dashboards
Module 10. Team Coordination and Handoffs
Align data science, engineering, and product teams. Streamline communication and reduce friction in deployment cycles.
12 chapters in this module
- Cross-team workflows
- Handoff documentation
- Model ownership
- SLA definitions
- Incident response
- Post-mortem process
- Tooling alignment
- Shared vocabulary
- Feedback loops
- Priority alignment
- Escalation paths
- Status reporting
Module 11. Documentation and Knowledge Transfer
Create living documentation that keeps pace with system changes. Ensure new team members can contribute quickly.
12 chapters in this module
- Architecture diagrams
- Runbook creation
- Onboarding guides
- Decision logs
- Incident archives
- System boundaries
- Dependency maps
- Change logs
- Knowledge base
- FAQ maintenance
- Glossary building
- Review cycles
Module 12. Long-Term Maintenance
Plan for ongoing updates, deprecation, and tech stack evolution. Avoid technical debt accumulation in AI systems.
12 chapters in this module
- Deprecation planning
- Tech debt tracking
- Framework updates
- Model lifecycle
- Version sunset
- User communication
- Backup strategies
- Data retention
- Compliance checks
- Audit readiness
- Review cycles
- Retirement process
How this maps to your situation
- Moving from prototype to production
- Managing rising inference costs
- Handling increased user load
- Improving system reliability
Before vs. after
Before
LLM prototypes that work in isolation but fail under real conditions, unpredictable costs, and fragile systems requiring constant firefighting.
After
Production-ready AI systems with stable performance, predictable costs, and clear operational playbooks for scaling and maintenance.
What's included with your purchase
- 12 modules with 12 chapters each (144 chapters)
- Downloadable templates and worked examples for every module
- Hand-built implementation playbook delivered alongside course access
- 30-day money-back guarantee
Delivery and format
- Course and learning environment access provisioned within 24 hours of purchase
- Hand-built implementation playbook delivered alongside course access
Format: Text-based modules and chapters in the Art of Service learning environment, plus downloadable templates and worked examples for every chapter, plus the hand-built implementation playbook delivered alongside course access.
Time investment: Approximately 3 hours per module, designed for incremental progress alongside active projects.
If nothing changes
Without a structured approach, teams waste months debugging issues that are already solved. Systems remain fragile, costs spiral, and technical debt accumulates, delaying future innovation.
How this compares to the alternatives
Unlike generic cloud certifications or academic courses, this focuses exclusively on real-world LLM production challenges , no theory, no filler. Compared to consulting, it’s faster to deploy and costs a fraction, with reusable frameworks.
Frequently asked
Who is this course for?
Engineers and tech leads responsible for deploying and maintaining LLM systems in production environments.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there video content?
No. The course is entirely text-based with downloadable templates and examples.
$199 one-time. Approximately 3 hours per module, designed for incremental progress alongside active projects..
Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.
30-day money-back guarantee·
144 chapters·
Hand-built playbook included·
Account access within 24 hours