Research model deployment: an engineering acceptance checklist

What demonstrates that deployment is complete?

Agree the environment, test inputs, expected outcomes, performance conditions and handover package before development. At acceptance, the receiving team should be able to start the system from documentation, run representative tasks, recognize failures and identify the deployed version.

Assess model quality separately from engineering usability. A page returning a result does not prove research validity; a model working on its author's computer does not establish reproducible deployment. This checklist sets a collaboration method, not universal performance thresholds.

1. Rebuild the environment

Record the operating system, runtime, dependencies, model weights, hardware and storage requirements. Document weight access and usage permissions, and separate configuration templates from credentials. Test installation and startup under the actual network constraints for an offline or campus deployment.

Pass condition: the receiving team starts the system in the agreed environment and identifies code and weight versions. Containers are one delivery option; Docker's build guidance is a reference for controlled builds. Having an image is not a substitute for running it.

2. Define inputs, outputs and API behavior

Specify types, required fields, size limits, output fields and task states. For long-running jobs, document identifiers, status queries, timeouts and cancellation. OpenAPI can describe requests and responses when an API is part of the scope.

Pass condition: the recipient independently invokes the system using handover examples. Fields match the documentation, and invalid, missing or oversized inputs produce understandable errors.

3. Check expected results and failures

Agree a permitted acceptance dataset with normal, boundary and expected-failure inputs. Record input identifiers, model versions, important parameters and comparison methods. For stochastic models, agree tolerances or evaluation methods rather than assuming byte-for-byte repeatability.

Pass condition: differences between actual and expected results are recorded and classified as model limitations, data issues or engineering defects. Failures are not shown as successes; retries and duplicate jobs behave as agreed.

4. Attach conditions to performance claims

Record hardware, input size, cold or warm state, concurrency and test duration before comparing latency, throughput or memory use. If a percentile target is agreed, document the sample size and calculation method. One small input does not establish sustainable service capacity.

Pass condition: the agreed workload meets the agreed thresholds, with defined behavior for resource limits, queues and timeouts. Confirm compute budget alongside capacity.

5. Bound access and data handling

Define who can submit jobs and view results, what logs contain, where data is stored and when it is removed. As relevant to the project, test unauthorized access, access to another user's results and temporary-file cleanup. Private research data must not become public demo inputs or log content.

Pass condition: access, storage and retention match the agreement. Operators know how to rotate credentials, restrict access and investigate issues.

6. Make handover usable

Provide installation instructions, configuration templates, API documentation, test records, known limitations and troubleshooting steps. Assign responsibility for code, weights, deployment scripts and recurring costs. Define and test update, rollback or backup procedures where required.

Pass condition: the designated recipient completes an independent operation, locates logs and follows the agreed recovery procedure. Maintenance scope and response arrangements are explicit.

A reusable acceptance record

1. Task and user: who performs which input-to-output workflow.
2. Environment and version: hardware, network, dependencies and weight identifiers.
3. Inputs and outcomes: source, comparison method, actual results and differences.
4. Failures and performance: scenarios, resource conditions, thresholds and evidence.
5. Access and handover: permissions, files, recipient, outstanding items and approval date.

Mark each item as passed, requiring correction or outside the agreed scope. Scope exclusions must be agreed beforehand rather than added unilaterally during acceptance.

Connect the checklist to a product workflow

The Neurosurgical Vascular Reconstruction System presents a specialist workbench interface. Use it to discuss the import, interaction, review and output steps in your own product; the case page does not replace model evaluation or performance testing.

Begin with model readiness and deployment options. For implementation, see model deployment and productization or send a deployment brief. Quotes depend on model readiness, API and interface scope, target environment, validation and maintenance requirements; compute and model improvement are agreed separately.