A tailored course, built for your situation
Deeper command of ML system design patterns in high-velocity environments
Build repeatable, production-grade ML architectures with precision and speed
$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.
Who this is for
Machine Learning Engineer working in a high-traffic, product-integrated environment where system reliability and speed-to-deploy are critical.
Who this is not for
Engineers focused only on research prototypes or academic exploration without production deployment goals.
What you walk away with
- Recognize and apply 12 proven ML system design patterns to new projects
- Make integration decisions faster using pre-analyzed component tradeoffs
- Reduce design rework by referencing battle-tested architecture decision records
- Explain system choices with confidence using standardized pattern language
- Anticipate failure modes and edge cases early in the design phase
The 12 modules (with all 144 chapters)
Module 1. Pattern: Streaming Inference Pipeline
A real-time inference architecture using Kafka, model routers, and dynamic batching. Covers latency SLAs, backpressure handling, and autoscaling triggers.
12 chapters in this module
- Event ingestion from edge services
- Message queue selection criteria
- Model router load balancing
- Dynamic batch size tuning
- Latency monitoring points
- Fallback model activation
- Schema drift detection
- Cold start mitigation
- GPU utilization thresholds
- Rolling deployment strategy
- Canary metric selection
- Version rollback triggers
Module 2. Pattern: Hybrid Batch-Streaming Feature Store
Unifies offline training and online serving with consistent feature computation. Addresses time travel, staleness, and backfill workflows.
12 chapters in this module
- Feature timestamp alignment
- Point-in-time correctness rules
- Online store indexing strategy
- Batch materialization cadence
- On-demand feature computation
- Schema evolution handling
- Drift detection thresholds
- Feature lineage tracking
- Serving latency targets
- Backfill pipeline design
- Consistency check frequency
- Access pattern optimization
Module 3. Pattern: Model Chaining with Conditional Routing
Routes inputs through multiple models based on confidence, domain, or cost. Used for fallbacks, escalation, and cost-aware inference.
12 chapters in this module
- Confidence threshold calibration
- Domain classifier training data
- Routing decision log schema
- Latency vs. accuracy tradeoff
- Fallback model selection
- Escalation path definition
- Cost-per-inference tracking
- Human-in-the-loop trigger
- Model timeout handling
- Error feedback loop design
- A/B test integration
- Routing audit trail
Module 4. Pattern: Shadow Mode Deployment
Deploys new models alongside existing ones without affecting live traffic. Used for validation before cutover.
12 chapters in this module
- Traffic mirroring setup
- Input duplication mechanism
- Output diff analysis
- Latency impact assessment
- Error rate comparison
- Drift detection in shadow
- Label availability timing
- Performance benchmark criteria
- Cutover decision checklist
- Rollback readiness test
- Staging environment parity
- Monitoring alert thresholds
Module 5. Pattern: Multi-Tenant Model Serving
Serves different business units or merchants with isolated configurations while sharing infrastructure. Balances cost and isolation.
12 chapters in this module
- Tenant identifier injection
- Configuration isolation model
- Resource quota enforcement
- Latency SLA per tenant
- Cost allocation tagging
- Model version per tenant
- Access control integration
- Tenant-specific drift detection
- Shared vs. dedicated GPU pools
- Cold start frequency per tenant
- Usage metering pipeline
- Tenant onboarding workflow
Module 6. Pattern: Incremental Model Training
Updates models with new data without full retraining. Reduces latency between data arrival and model update.
12 chapters in this module
- Delta data ingestion
- Model warm-start compatibility
- Versioned checkpoint storage
- Training convergence criteria
- Data drift detection
- Feature store slice queries
- Incremental validation set
- Backfill necessity check
- Bias monitoring over time
- Training trigger automation
- Checkpoint retention policy
- Rolling retrain schedule
Module 7. Pattern: Canary Model Evaluation
Tests model performance on a fraction of live traffic before full rollout. Uses business-aligned metrics for go/no-go decisions.
12 chapters in this module
- Traffic allocation mechanism
- Business metric alignment
- Statistical significance threshold
- Error type weighting
- User cohort selection
- Performance delta alerting
- Bias shift detection
- Fallback readiness check
- Evaluation duration setting
- Manual review trigger
- Automated rollback conditions
- Stakeholder notification protocol
Module 8. Pattern: Model Registries with Governance Hooks
Tracks model versions, metadata, and compliance status. Integrates with CI/CD and deployment gates.
12 chapters in this module
- Version metadata schema
- Owner and maintainer fields
- Training data lineage
- Fairness assessment flag
- Documentation completeness check
- CI/CD integration point
- Deployment gate logic
- Retention policy rules
- Access audit logging
- Model deprecation workflow
- Security scan integration
- Approval routing rules
Module 9. Pattern: Edge Model Deployment
Deploys models to client devices or edge nodes. Focuses on size, latency, and update resilience.
12 chapters in this module
- Model quantization techniques
- Device capability detection
- Update download strategy
- Background sync scheduling
- Offline inference handling
- Battery impact assessment
- Model size budget per OS
- Rollout by device tier
- Integrity verification method
- Fallback to server
- Usage telemetry collection
- Silent update mechanism
Module 10. Pattern: Feedback Loop Automation
Captures model predictions and actual outcomes to trigger retraining. Closes the loop between inference and learning.
12 chapters in this module
- Outcome label capture
- Prediction ID propagation
- Delay tolerance window
- Feedback aggregation interval
- Retraining trigger logic
- Data quality validation
- Manual label review queue
- Drift correlation analysis
- Feedback loop latency
- Model version alignment
- Bias in feedback detection
- Automated alert thresholds
Module 11. Pattern: Cross-Stack Observability
End-to-end visibility from user action to model input to inference to outcome. Detects silent failures and data corruption.
12 chapters in this module
- Trace ID propagation
- Model input logging
- Feature value tracking
- Prediction consistency check
- Outcome reconciliation
- Data drift alerting
- Latency outlier detection
- Error rate correlation
- Dashboard integration
- Incident response playbook
- Log retention policy
- Access control for logs
Module 12. Pattern: Zero-Downtime Model Rollout
Updates models without service interruption. Uses blue-green or rolling strategies with live traffic validation.
12 chapters in this module
- Deployment strategy selection
- Traffic shift increments
- Health check integration
- Monitoring during transition
- Rollback trigger conditions
- Version coexistence window
- Configuration sync process
- DNS update coordination
- Load balancer rule update
- Client retry behavior
- Cache invalidation timing
- Post-rollout validation
How this maps to your situation
- When designing a new ML service from scratch
- When refactoring a legacy model pipeline
- When scaling an existing model to new traffic volumes
- When integrating ML into a customer-facing product flow
Before vs. after
Before
Spending time reinventing system designs, debating component choices, and discovering integration issues late.
After
Starting each new project with a clear pattern in mind, adapting proven designs, and moving faster with confidence.
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: 8, 10 hours to complete all modules, or 30, 45 minutes per module for targeted learning.
How this compares to the alternatives
Unlike generic ML courses focused on algorithms or theory, this course delivers production-proven system designs with component-level decision logic, integration specifics, and failure mode analysis used by top-tier engineering teams.
Frequently asked
Is this course about model development or system architecture?
It focuses on system architecture, how to structure end-to-end ML systems for production reliability, scalability, and maintainability.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Are the examples based on real production systems?
Yes, each pattern is drawn from documented deployments at high-velocity tech companies with traffic at scale.
$199 one-time. 8, 10 hours to complete all modules, or 30, 45 minutes per module for targeted learning..
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