AI and algorithm research should not begin with a model name or an unsupported final metric. A defensible workflow moves through task definition, data review, baseline reproduction, feasibility validation, error analysis, engineering implementation, deployment assessment and reproducible delivery. Every stage records the sample, environment, version and metric; conditions outside the evidence are marked as not verified.
Stage 1: define the research problem as an algorithm task
Determine whether the task is classification, regression, prediction, detection, segmentation, optimization, control or another form, and explain how the output supports the research workflow. A model metric is not automatically the project outcome. Better detection, for example, does not by itself prove that downstream review or device control is ready for use.
The definition also needs the cost of error. False negatives and false positives can have different consequences. Numeric error must be interpreted against range and use. Planning and control tasks add stability, safety boundary and recovery requirements.
Stage 2: review data before training
A more complex model cannot repair every data problem. If samples do not represent the operating scenario, labels use inconsistent rules, or the test set is repeatedly tuned against, the reported metric loses meaning. The review should produce a documented issue and collection plan.
- Confirm sources, authorization, acquisition conditions and time range.
- Check missing, duplicate and exceptional values, label consistency and class distribution.
- Detect leakage when the same object appears across development and test sets.
- Record preprocessing, augmentation, filtering and exclusion rules.
- Preserve the relationship between raw data, processing scripts and dataset versions.
Stage 3: establish a reproducible baseline
A baseline can be a simple rule, a classical method, existing code or a basic model. Its purpose is not the highest number. It confirms that loading, splitting, evaluation and runtime can be repeated. Code, configuration, random seeds, dependencies and logs belong in version control.
Once the baseline is stable, a team can determine whether architecture, features, loss functions or training strategy produce a real improvement. Controlled experiments should change interpretable factors and retain failed trials rather than publish only favorable runs.
Stage 4: let error analysis drive the next action
| Error source | Question to inspect | Possible action |
|---|---|---|
| Coverage | Do failures concentrate by device, time or object? | Collect data or narrow the supported scope |
| Labels | Are similar samples annotated consistently? | Revise the rule and relabel a sample |
| Model | Are errors linked to scale, occlusion, noise or long tails? | Change features, architecture or loss |
| Evaluation | Do threshold, metric and test set match the real decision? | Revise metric and operating rule |
| Deployment | Did conversion, quantization or hardware change outputs? | Run layer-level or end-to-end consistency tests |
Stage 5: engineering and deployment assessment
Engineering delivery turns experiment code into maintainable modules with defined inputs, outputs, exceptions, logs, concurrency and resource limits. Deployment records the OS, compute hardware, driver, framework, model format and preprocessing versions. The existence of a current framework release is not evidence that the project has validated it.
Edge or real-time systems also need startup, continuous run, memory, power, latency distribution and recovery tests. Every performance result should be tied to a device, sample and method; one demonstration does not establish long-term stability.
Final package and evidence boundary
Results apply to the recorded samples, environment and version. A new device, location, population, season or operating condition may require new validation. Research engineering can supply implementation and evidence, but a stage result should not be presented as universal performance.
- Dataset versions, split and processing records.
- Training and inference code, configuration, weights and environment.
- Metrics, evaluation scripts, baseline and error analysis.
- Deployment instructions, interfaces, resource tests and limitations.
- Reproduction steps, artifact hashes and support boundary.
Frequently Asked Questions
Must feasibility validation reach the final target?
Not necessarily. Its purpose is to test whether the data, technical route and evaluation method are viable and to identify error sources and conditions for further investment.
Why is a model metric not enough for acceptance?
The metric must be tied to a test set, formula, threshold, version and hardware, and it must represent the real workflow and cost of error.
Does a deployed model need another test?
Yes. Conversion, quantization, drivers, hardware and preprocessing can change outputs. Revalidate function and performance in the target environment.
Next step
If data, a published method, code or a model already exists, begin with data review and baseline reproduction to establish feasibility, metric definitions and deployment constraints.