Manage Creation of the AI Solution Deployment Plan
Key Takeaways
- ECO Domain V, Task 1 sits at the front of CPMAI Phase 6 Model Operationalization: you manage creation of a written deployment plan, you do not write the deployment scripts.
- AI deployments favor shadow mode and canary rollout because offline evaluation cannot fully predict live behavior, and a model can run flawlessly while still being wrong.
- Production inference infrastructure is a different problem from training infrastructure: plan peak capacity, a latency budget, scaling behavior, and cost per prediction at real volume.
- A usable rollback procedure names the trigger threshold, the person who can call it alone, the tested time to restore, and what happens to decisions the model already made.
- On the exam, a deployment plan without a tested rollback path is incomplete regardless of how good the model's evaluation results were.
What Domain V, Task 1 Asks of You
CPMAI Phase 6, Model Operationalization, does not begin when an engineer copies a model into production. It begins with a written deployment plan stating how the model will reach live users, what must be true before it does, and how the organization gets back to safety if it misbehaves. Exam Content Outline (ECO) Domain V, Task 1 makes this a management deliverable: you manage its creation, you do not author the deployment scripts.
A model that cleared the Phase 5 readiness gate has been proven on held-out data inside a controlled environment. Production is a different environment: live data, real latency, real integrations, real consequences. Offline evaluation cannot fully predict live behavior, and the deployment plan is where you close that gap deliberately rather than discovering it on go-live night.
Selecting a Deployment Strategy and Timeline
Strategy is the first decision, because timeline, infrastructure and rollback all follow from it.
| Deployment strategy | How it works | When to choose it | Main limitation |
|---|---|---|---|
| Shadow mode | The model scores live traffic alongside the incumbent process, but its output is logged rather than acted on | A first AI deployment, or high-consequence decisions such as credit, clinical triage or claims denial, where you need live evidence before anyone relies on the output | Costs full inference spend for zero business benefit, and proves nothing about user adoption because no user ever sees the output |
| Canary / phased rollout | A small slice of real traffic (one region, one product line, five percent of cases) is served by the model, then widened in steps against promotion criteria | Most AI go-lives, when you want real business effect measured before full exposure | Slower, and a small slice may not contain enough rare events such as fraud or equipment failure to judge performance quickly |
| Blue-green | Two complete environments run in parallel and traffic is switched from old to new in a single move, reversible in one move | Replacing an existing model version where the interface is unchanged and instant reversal matters | Doubles infrastructure across the cutover, and validates the release mechanics rather than the model's business behavior |
| Full cutover | Every user moves to the model at once on a set date | Low-consequence, reversible, internally facing uses, or where a parallel run is genuinely impossible | Every error is a production error at full scale; the only recovery is rollback, and there is no smaller failure to learn from first |
AI deployments favor shadow and canary for a structural reason: conventional software either works or throws an error, whereas a model can execute perfectly and still be wrong. Shadow and canary buy live evidence at bounded cost. Your timeline then states the shadow window length, the canary steps and dwell time at each step, the promotion criteria between steps, and the date the incumbent process is retired.
Planning Production Infrastructure and Resource Allocation
Production inference capacity is requested back in Domain III, where the separation between training capacity and serving capacity is set out in full. The deployment plan does not re-argue it — it confirms that capacity was provisioned and pins down three numbers the rollout itself depends on:
- Peak inference capacity, not average. Month-end claims surges, Monday-morning demand forecasting runs and seasonal retail spikes define the ceiling each promotion step must clear before traffic is widened.
- Latency budget, expressed end to end for the business transaction, with the model allocated an explicit slice. It becomes a go-live validation criterion and usually a pause threshold too, so it has to be a number before the window opens.
- Cost per prediction at real volume, multiplied out to an annual run cost alongside monitoring, storage and periodic retraining. A model that is economic at canary volume can be uneconomic at full rollout, and that is a decision to take before widening rather than after.
Resource allocation is equally concrete. Name the team that operates the solution after go-live, the on-call rotation, and the budget line that funds inference. An AI solution with no named operator is a transition failure already in progress.
Coordinating with IT, Security and Integrated System Owners
Every system that feeds the model or consumes its output has an owner whose release calendar you do not control. Enumerate them all: source systems, the serving platform, identity and access management, the case-management or customer relationship management system where the prediction lands, and the monitoring stack. Get each owner's dated commitment into the plan.
Then reconcile with the organization's release governance: change advisory board submission deadlines, change-freeze windows such as quarter-end financial close, peak trading periods or clinical system freezes, and the security review that must complete before an endpoint is exposed. Bring security into planning rather than into the final week, or the review becomes your critical path.
Defining a Rollback Procedure That Works
The sentence "roll back if there is a problem" is not a procedure. A usable one answers five questions:
- Trigger. The observable condition that starts a rollback, written as a threshold plus a duration and agreed before go-live.
- Authority. Who can call it alone, at 2 a.m., without convening a committee.
- Mechanism and time to restore. The steps and the elapsed time, tested at least once outside production.
- Disposition of decisions already made. This is the AI-specific question. Predictions the model already issued do not vanish when you revert the endpoint. Which need reversal, human re-review, customer notification or regulatory disclosure? A claims model that denied 400 claims during three bad hours creates remediation work that outlives the rollback itself.
- Communication. Who informs users, the sponsor and affected customers, and in what words.
Contingency planning extends this into the degraded states short of full reversal: routing to the manual process, falling back to the previous model version, or serving a simple rules baseline while the issue is diagnosed.
Checklists and Go-Live Validation Criteria
The deployment checklist turns the plan into verifiable line items with owners: approvals recorded, model version registered with its documentation, infrastructure provisioned, security review closed, upstream feeds confirmed, integrations tested end to end, rollback rehearsed, monitoring and alerting live, support staff briefed. The validation criteria are the pass/fail tests the deployment must satisfy before it is declared successful: output parity against the validated model, latency at the agreed percentile, error rate, output distribution inside an expected band, and confirmation that downstream systems received usable records.
Agree both in advance, while nobody is under go-live pressure. On the exam, a deployment plan without a tested rollback path is incomplete regardless of model quality, and strong evaluation metrics are never the answer to the question "is this ready to deploy?"
A claims triage model passed the Phase 5 readiness gate with strong evaluation results. The sponsor wants a single go-live next Monday that routes all 40,000 monthly claims through the model, arguing that the evaluation already proved the model works. What should the project manager recommend?
The deployment plan for a claims triage model sets out the canary steps, the rollback procedure and the go-live date. Its validation criteria section states only that the deployment will be declared successful once the model is serving traffic without errors. Which addition most improves the plan?
The rollback section of a deployment plan for a credit decisioning model reads: "If issues arise, the platform team will revert to the previous release." Which addition most improves the plan?