A tailored course, built for your situation
Mastering AI DevOps and Cost-Optimized Architecture
Build scalable, secure, and cost-efficient AI systems with proven DevOps and architectural patterns
$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.
Spending too much time debugging inefficient AI pipelines or over-provisioned cloud resources?
The situation this course is for
AI teams today face mounting pressure to deliver fast, reliable models while cloud bills spiral. The root cause isn't technology, it's architecture. Without deliberate design, retrieval pipelines, model serving, and infrastructure scale inefficiently, creating technical debt and financial drag. This course targets the architectural and operational decisions that separate sustainable AI systems from costly experiments.
Who this is for
AI Engineers, Platform Engineers, and DevOps professionals leading or contributing to production AI systems with a focus on cost, reliability, and scalability
Who this is not for
Individuals focused only on theoretical AI research or non-technical governance roles without hands-on deployment responsibilities
What you walk away with
- Architect cost-efficient AI systems using proven design patterns
- Optimize retrieval and generation pipelines in RAG architectures
- Implement CI/CD and IaC for AI workloads across multi-cloud environments
- Apply observability to detect and correct performance and cost drift
- Deploy agentic AI systems with built-in cost controls and fail-safes
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI DevOps
Establish core principles of DevOps as applied to AI and machine learning workflows, including model lifecycle management and team coordination patterns.
12 chapters in this module
- What is AI DevOps
- Model lifecycle phases
- Team topology patterns
- Versioning data and models
- Environment parity
- Reproducibility standards
- Pipeline automation basics
- Testing in AI systems
- Security in AI pipelines
- Compliance considerations
- Toolchain selection
- Measuring deployment velocity
Module 2. RAG Architecture Deep Dive
Explore retrieval-augmented generation systems with a focus on independent retrieval pipelines, chunking strategies, and relevance tuning.
12 chapters in this module
- RAG vs traditional search
- Retriever types comparison
- Chunking strategies
- Embedding model selection
- Vector database choices
- Query rewriting techniques
- Re-ranking essentials
- Latency vs accuracy tradeoffs
- Caching retrieval results
- Handling hallucination
- Evaluation metrics
- Scaling retrieval independently
Module 3. Cost-Aware Architecture Design
Learn how to design systems where cost is a first-class constraint, using architectural decisions to reduce waste across compute, storage, and data transfer.
12 chapters in this module
- Cost as architecture driver
- Compute tiering strategies
- Spot instance usage
- Model quantization benefits
- Inference batching
- Cold start mitigation
- Storage lifecycle policies
- Data transfer optimization
- Monitoring spend per request
- Auto-scaling with cost caps
- Right-sizing containers
- Architecture review checklist
Module 4. Infrastructure as Code for AI
Implement repeatable, auditable infrastructure provisioning using Terraform and cloud-native tools tailored for AI workloads.
12 chapters in this module
- IaC principles recap
- Terraform for AI stacks
- Module design patterns
- State management best practices
- Cloud provider integration
- Secrets management
- Policy as code
- Drift detection
- CI integration
- Testing infrastructure changes
- Rollback strategies
- Multi-environment setup
Module 5. CI/CD Pipelines for AI
Build robust continuous integration and deployment workflows that handle model updates, data changes, and infrastructure modifications safely.
12 chapters in this module
- Pipeline design principles
- Trigger strategies
- Model validation gates
- Data drift detection
- Canary release patterns
- Blue-green for AI
- Rollback automation
- Approval workflows
- Pipeline observability
- Testing data quality
- Model performance checks
- Pipeline security
Module 6. Observability in AI Systems
Implement logging, monitoring, and tracing across AI pipelines to detect issues in retrieval, generation, and infrastructure performance.
12 chapters in this module
- Observability pillars
- Structured logging setup
- Metrics collection
- Distributed tracing
- Alerting strategies
- Model performance dashboards
- Latency breakdown
- Error rate tracking
- Cost per inference
- User feedback loops
- Anomaly detection
- Root cause analysis
Module 7. Multi-Cloud AI Deployment
Design and operate AI systems across multiple cloud providers to optimize cost, availability, and regulatory compliance.
12 chapters in this module
- Cloud provider comparison
- Workload placement rules
- Cross-cloud networking
- Data sovereignty rules
- Cost benchmarking
- Vendor lock-in mitigation
- Unified monitoring setup
- Failover across clouds
- Identity federation
- Billing integration
- Compliance alignment
- Exit strategy planning
Module 8. Agentic AI Patterns
Implement agent-based AI systems with clear boundaries, cost controls, and safety mechanisms to prevent runaway execution.
12 chapters in this module
- Agent role definition
- Tool selection patterns
- Memory management
- Planning algorithms
- Execution safety
- Cost per agent step
- Concurrency limits
- Human-in-the-loop
- Agent collaboration
- Failure mode analysis
- Audit logging
- Agent lifecycle management
Module 9. Security and Compliance for AI
Apply security best practices and compliance frameworks to AI systems, focusing on data privacy, model integrity, and access control.
12 chapters in this module
- Data classification
- PII detection
- Model access control
- Prompt injection defense
- Output filtering
- Audit logging
- Compliance frameworks
- GDPR considerations
- Model explainability
- Red teaming AI
- Vulnerability scanning
- Incident response
Module 10. Scaling AI Systems
Learn strategies to scale AI workloads efficiently, balancing performance, cost, and reliability across growing user demand.
12 chapters in this module
- Load testing methods
- Auto-scaling configuration
- Database optimization
- Caching layers
- Content delivery networks
- Request queuing
- Rate limiting
- Graceful degradation
- Capacity planning
- Burst handling
- Multi-region deployment
- Performance budgeting
Module 11. Model Lifecycle Management
Manage the full lifecycle of AI models from development to deprecation, ensuring consistency, traceability, and compliance.
12 chapters in this module
- Model registry setup
- Versioning strategy
- Metadata standards
- Approval workflows
- Deployment tracking
- Performance monitoring
- Drift detection
- Retraining triggers
- Model retirement
- Audit trail maintenance
- License compliance
- Stakeholder communication
Module 12. Sustainable AI Operations
Establish long-term operational practices that ensure AI systems remain efficient, maintainable, and aligned with business goals.
12 chapters in this module
- Operational reviews
- Cost reporting
- Team training
- Documentation standards
- Incident post-mortems
- Improvement backlogs
- Knowledge sharing
- Tooling updates
- Architecture evolution
- Feedback integration
- Stakeholder reporting
- Continuous learning
How this maps to your situation
- You're scaling AI systems and noticing cost spikes
- Your team is adopting agentic AI patterns
- You're responsible for platform stability and efficiency
- You need to justify cloud spend to leadership
Before vs. after
Before
Spending cycles on debugging inefficient pipelines and justifying cloud costs without clear architectural guidance
After
Deploying AI systems with built-in cost controls, observability, and repeatable processes that scale efficiently
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 45-60 hours total, designed to be completed at your pace over 6-8 weeks with practical implementation between modules.
If nothing changes
Without deliberate architectural choices, AI systems become increasingly expensive and fragile, leading to project overruns, stakeholder distrust, and technical debt that slows innovation.
How this compares to the alternatives
Unlike generic DevOps courses, this program focuses specifically on AI systems, combining LLMOps, cost-aware design, and agentic patterns. Compared to vendor-specific training, it offers cloud-agnostic principles with implementation flexibility.
Frequently asked
Who is this course for?
AI Engineers, Platform Engineers, and DevOps professionals building and operating production AI systems.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there a money-back guarantee?
Yes, 30-day money-back guarantee if the content doesn't meet your expectations.
$199 one-time. Approximately 45-60 hours total, designed to be completed at your pace over 6-8 weeks with practical implementation between modules..
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