Select defensible use cases
Choose work where AI can improve a real operational decision with acceptable risk.
PRODUCTION AI PRACTICE
AI Deployment
Move models, agents, and retrieval systems from prototype into governed workflows that users can trust, operators can support, and leaders can evaluate.
01 / CAPABILITY OUTCOMES
What you will be able to do.
This path is for ML engineers, product engineers, technical leaders, and implementation teams responsible for outcomes after the prototype.
Evaluate before launch
Create scenario suites that expose task errors, unsafe outputs, and workflow failures.
Design controlled workflows
Combine models with deterministic logic, tools, permissions, and human review.
Ship governed systems
Build auditability, data protection, model change control, and rollback into production.
Earn calibrated trust
Help users understand confidence, sources, limits, and when to escalate.
Measure the whole system
Track model quality alongside latency, cost, adoption, overrides, and business outcomes.
02 / CURRICULUM
A field-oriented learning sequence.
Each module produces a reusable work product. The course is complete only when the artifacts connect into one coherent deployment record.
Choose an operational AI use case
Separate useful AI opportunities from tasks better solved with rules, search, analytics, or workflow redesign.
- Decision and workflow mapping
- AI suitability and risk classification
- Baseline cost and value hypothesis
FIELD OUTPUT · AI use-case brief
Design the data and knowledge layer
Build a traceable foundation for prompts, retrieval, features, context, and permissions.
- Data provenance and access boundaries
- RAG corpus design and versioning
- Structured context and tool contracts
FIELD OUTPUT · Data and knowledge specification
Build an evaluation system first
Define what good, harmful, and uncertain outputs look like before optimizing the model.
- Golden sets and scenario suites
- Task, safety, and workflow metrics
- Human review and adjudication
FIELD OUTPUT · Evaluation plan and baseline
Engineer the production workflow
Connect models to tools, users, approvals, and deterministic safeguards.
- Agent and tool orchestration
- Fallbacks, timeouts, and idempotency
- Human-in-the-loop patterns
FIELD OUTPUT · Production AI architecture
Govern security and failure modes
Control data exposure, prompt injection, unsupported actions, model drift, and policy violations.
- Threat modeling and red teaming
- Access control and audit trails
- Model and prompt change governance
FIELD OUTPUT · AI risk and control register
Release for adoption
Pilot with bounded users and decisions, train reviewers, and establish ownership for correction and escalation.
- Shadow, assistive, and controlled-autonomy modes
- Reviewer calibration and UX
- Rollout gates and rollback criteria
FIELD OUTPUT · Pilot and release plan
Monitor realized value
Track quality, cost, latency, adoption, overrides, and business outcomes after launch.
- Online evaluation and drift signals
- Cost and latency budgets
- Value realization and scale decision
FIELD OUTPUT · AI operating review
03 / PRACTICE
Apply the method under pressure.
Each mission introduces incomplete information, conflicting incentives, and a complication after the first design decision.
FOUNDATIONFIELD SERVICE
Titan Industrial
Deploy a source-grounded knowledge assistant that remains practical without promoting unsafe repair shortcuts.
PROOF OF CAPABILITYRAG evaluation suite, controls, pilot, and operating review
Open mission →INTERMEDIATEINSURANCE
Apex Insurance
Build an assistive claims-triage system with auditability, bias investigation, and mandatory human authority.
PROOF OF CAPABILITYTriage workflow, risk register, and model governance pack
Open mission →ADVANCEDMANUFACTURING
Northstar AI Investigator
Combine sensor evidence, maintenance records, and operator notes while clearly separating correlation from causation.
PROOF OF CAPABILITYEvidence-grounded investigation copilot and adoption plan
Open mission →04 / ASSESSMENT
Code is necessary. It is not sufficient.
Work is evaluated on whether it could survive inside a real organization and improve a real operational decision.
| Dimension | What strong work demonstrates | Weight |
|---|---|---|
| Use-case judgment | The selected AI intervention is justified against simpler alternatives and bounded to a real workflow. | 15% |
| Evaluation quality | Tests reflect realistic errors, safety risks, uncertainty, and user behavior. | 20% |
| System architecture | Models, retrieval, tools, controls, and human review form a reliable workflow. | 20% |
| Governance | Security, privacy, auditability, change control, and rollback are operationalized. | 15% |
| Adoption design | User trust, reviewer calibration, interface design, and escalation are addressed. | 15% |
| Value realization | Quality, cost, latency, usage, and business impact support the scale decision. | 15% |
PRODUCTION AI PRACTICE
Deploy AI that can be trusted with work.
This path is for ML engineers, product engineers, technical leaders, and implementation teams responsible for outcomes after the prototype.