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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →Start with the decision someone needs to make, not with a model. Define the outcome you want to improve, what information is available when the decision happens, what errors cost, and whether a simpler approach can do the job. Only then decide whether machine learning (ML) is appropriate—and, if it is, what kind of ML task to build.
1. Describe the decision in plain language
Write down who has the problem, what is difficult or costly today, and which decision needs to improve. Include practical constraints such as timing, budget, privacy, and who will act on a result. Avoid starting with a proposed algorithm, vendor, or product; those are possible means, not the problem itself.
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For example, “A clinic wants to reduce missed appointments” describes an outcome and a setting. “Build a neural network” does not say who uses its output, what action follows, or whether that action is useful.
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2. Define success before modeling
Pair a real user or business outcome with technical measures and a baseline. The outcome might be fewer missed appointments or shorter customer-service handling time. The technical measures should show how well a proposed system performs, while the baseline establishes what happens with the current process or a straightforward alternative. The University of British Columbia’s project-framing guidance recommends clarifying the goal, whether ML is needed, what to predict, how to measure success, the baseline, the operating point, and the value of improvement (UBC project-framing guidance, 2024).
Specify the operating point—the threshold or decision rule at which a prediction triggers action—because performance depends on how the model is used. A score that looks strong in isolation may not create value if acting on its predictions costs more than the errors it prevents. Agree with stakeholders on what improvement is meaningful before comparing models.
3. Choose a task representation
Describe the target, the time period covered by a prediction, and what counts as an acceptable mistake. The task type follows from the decision, not from a preference for a particular model.
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- Classification: predict a discrete category, such as whether an appointment will be missed.
- Regression or forecasting: predict a numeric value, such as demand next week or expected handling time.
- Ranking or recommendation: order items or suggest options when relative priority matters more than a single numeric estimate.
- Clustering: group examples when there is no known target label and the goal is to discover useful structure.
For supervised learning, state precisely what the label means and how it will be assigned. For unsupervised work, state what a useful grouping would enable someone to do. Google’s official Introduction to Machine Learning Problem Framing course organizes the work around deciding whether ML is a good approach, outlining the ML problem, selecting a model, and defining success metrics (Google for Developers, last updated 2025). The design-pattern reference also recommends asking whether the task is supervised or unsupervised, what the features and labels are, and how much error is acceptable (Machine Learning Design Patterns, 2020).
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Before committing to ML, establish that the needed data exists or can be collected, that labels are reliable and affordable, and that every feature will be available at the moment a decision must be made. A feature recorded only after the outcome occurs cannot legitimately support an earlier prediction. Also check whether the examples represent the people, devices, locations, and conditions expected in deployment.
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Raw data is not the same as usable training data: labeling can be costly, models depend on context, and data gathered under different conditions may not transfer to the real setting. The Edge AI Fundamentals chapter from Edge Impulse’s Deep Learning Bible discusses these feasibility limits and the risk of failure on unfamiliar conditions.
5. Compare ML with a simpler alternative
Test whether a rule, formula, search method, workflow change, or human process can meet the goal. A deterministic rule may be cheaper to build, easier to explain, and simpler to maintain. ML is more compelling when relationships are complex or noisy, there are many variables, or hand-coded rules would be impractical to discover and maintain.
Use a comparison that reflects the actual decision rather than model accuracy alone:
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|---|---|
| Decision benefit | Will this approach improve an outcome users or the organization value? |
| Data and labels | Can representative examples and trustworthy labels be obtained at a sustainable cost? |
| Error costs and threshold | What happens after a false alarm or a missed case, and where should action begin? |
| Explainability and auditability | Can affected people and operators understand or review the decision well enough? |
| Robustness | Will the approach remain useful when inputs or conditions change? |
| Operations | Can it meet latency and reliability needs, and can the team support and maintain it? |
| Privacy, security, and ethics | Are collection and use of the data acceptable, and could the system create unjustified harm? |
ML produces probabilistic outputs and can fail on inputs unlike its training data. If the application requires provable behavior, cannot tolerate uncertainty, or cannot explain decisions to the degree its users and obligations require, a conventional approach may be preferable. The Edge Impulse chapter also flags data needs, explainability, bias, and unfamiliar inputs as important limitations; UBC recommends an explicit ethics discussion before a project is committed.
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6. Design evaluation and ongoing checks
Evaluate against the baseline with data that reflects deployment. If the problem changes over time, use a time-aware split rather than allowing future information to leak into training or evaluation. Select measures that reflect error costs and the chosen operating point, then check whether results differ across relevant groups or operating conditions.
Plan monitoring as part of the solution, not as a later add-on. Inputs and outcomes can shift after deployment, and a model that worked in one setting may not work in another. Edge Impulse describes an iterative workflow that tests and refines the application, dataset, algorithms, and hardware together. Define who will notice degradation, what evidence triggers review, and whether the response is to retrain, change the threshold, revert to a baseline, or stop automated use.
7. Make the go/no-go decision explicit
Proceed with ML only when the expected improvement in the decision justifies the costs of data collection, labeling, engineering, operation, support, and ethical safeguards. Record the chosen target, available inputs, baseline, evaluation plan, operating point, and the reason a simpler alternative is insufficient. If ML does not yet clear that bar, document the non-ML solution and the evidence—such as better labels or a change in task conditions—that would make reconsideration worthwhile.
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