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Start with the decision someone needs to make—not with a model. Define the outcome you want to improve, the information available when the decision happens, the result the system should produce, and the cost of getting that result wrong. Then compare machine learning (ML) with a simple rule or existing process. ML is worthwhile only if it can improve the decision enough to justify the data, engineering, maintenance, and ethical costs.
1. Describe the decision in plain language
Identify who faces the problem, what currently goes wrong, and what decision or action needs to improve. Include practical constraints such as timing, available staff, privacy requirements, and the consequences of delay. Avoid choosing a model or vendor before you know what the system is meant to help someone do.
For example, “reduce missed appointments” describes a desired outcome. “Predict which patients will miss their next appointment so staff can offer timely reminders” is closer to a decision problem: it names the prediction, its intended use, and a possible intervention. The prediction matters only if staff can act on it and the action can improve the outcome.
2. Define success and a baseline before modeling
Pair a real user or business outcome with technical measures. A model score alone does not show that the system is useful: a prediction must lead to an action that improves the result compared with what happens now. The University of British Columbia’s project-framing guide recommends clarifying the goal, baseline, operating point, measurement approach, and value of improvement before committing to ML (UBC, 2024).
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- Baseline: What result does the current process, a simple rule, or a conventional method achieve?
- Technical measures: Which model measures relate to the outcome, and how will you evaluate them?
- Operating point: Where will you set a threshold or otherwise decide which predictions trigger action?
- Value: How much improvement would justify the added cost and complexity?
Set the operating point in light of the consequences of false alarms and missed cases. For instance, sending an unnecessary reminder and failing to contact someone who needs one may have very different costs. Those costs shape which errors matter and how much confidence to require before acting.
3. Choose the task that matches the desired output
Once the decision and success criteria are clear, specify what the system should predict or produce. State the label or target precisely, the prediction horizon, and what counts as an acceptable error. The framing questions in Machine Learning Design Patterns include whether the task is supervised or unsupervised, what features and labels are involved, and how much error is tolerable (O’Reilly, 2020).
Rank #2
- Classification: Predict a discrete category, such as whether an appointment will be missed.
- Regression or forecasting: Estimate a numeric value, such as expected handling time or demand over a defined future period.
- Ranking or recommendation: Order items or suggest options when their relative priority matters.
- Clustering: Group examples when there is no known target label and the goal is to discover useful structure.
These task types are not interchangeable. If staff need a ranked list, a binary yes-or-no output may not fit their workflow. If the goal is to discover groups rather than predict a known outcome, a labeled classification task may be the wrong framing.
4. Check whether usable data and labels exist
A problem may be well suited to prediction in theory but infeasible with the available data. Check that the necessary examples can be collected, that labels are dependable and affordable to create, and that the input features will be available before the decision. Data recorded only after an event cannot support a prediction intended to happen beforehand.
Rank #3
- Availability: Are there enough relevant examples, or can they be collected without unacceptable cost?
- Label quality: Can people or systems assign the target consistently? How much effort will labeling require?
- Timing: Will every feature be available at the moment the prediction is needed?
- Representativeness: Do the examples reflect the people, devices, locations, and conditions where the system will be used?
- Transfer: Could differences between the data-collection setting and deployment setting make learned patterns unreliable?
Edge Impulse’s guidance on edge-AI projects stresses that raw data is not enough: labeling takes work, models depend on context, and data collected under different conditions may not transfer to deployment (Edge Impulse, n.d.). Consider whether the target itself is meaningful and fair to predict, especially where the resulting action affects people.
5. Compare ML with rules and other simpler options
ML is most defensible when the outcome is measurable, representative examples are available, and the relevant relationship is too complex, noisy, or high-dimensional for practical hand-coded rules. It can learn useful approximations from data when conventional modeling is difficult. But a deterministic rule, formula, search method, workflow change, or human process may meet the goal with less maintenance and clearer behavior.
Edge Impulse’s chapter advises considering ML when relationships are complex or noisy, rules would be prohibitively difficult to discover, or there are many variables. It also emphasizes the need for quality data, tolerance for probabilistic output, acceptable explainability, and inputs that resemble the training data (Edge Impulse, n.d.).
| Approach | More suitable when | Trade-off to examine |
|---|---|---|
| Rule, formula, or workflow change | The desired behavior is known and can be expressed reliably without learning patterns from examples. | Rules may become difficult to maintain when cases are numerous, noisy, or full of exceptions. |
| Machine learning | There are usable examples and a complex or noisy relationship to predict, rank, recommend, or group. | It requires data and evaluation, produces probabilistic results, and may be harder to explain or maintain. |
| Human decision process | Context, judgment, or accountability is central and the volume permits meaningful human review. | Availability, consistency, speed, and workload may constrain the process. |
The phrase “the best ML is no ML at all,” quoted from Edge Impulse principal ML engineer Mat Kelcey, captures a useful discipline: do not add a model unless it solves a problem better than a simpler alternative. It is not a reason to reject ML categorically.
Best Value
6. Evaluate for the way the system will actually be used
Compare any proposed model with the baseline using data and evaluation methods that resemble deployment. For data collected over time, a time-aware evaluation can better reflect future use than a random split that mixes earlier and later examples. Set the threshold or ranking behavior to match the real cost of errors, then assess whether the resulting actions improve the user or business outcome.
Before launch, plan to monitor changes in incoming data and failures across relevant subgroups. A model that performed acceptably on one sample may not remain reliable as conditions change. Edge Impulse describes an iterative workflow that tests and refines the application, dataset, algorithms, and hardware rather than treating model training as a one-time finish (Edge Impulse, n.d.). Include privacy, security, explainability, and ethical or regulatory acceptability in the evaluation, not as afterthoughts.
7. Make the go/no-go decision explicit
Proceed with ML only when the expected improvement in decisions or outcomes is worth the data, engineering, support, privacy, and ethical costs. Google’s official course on problem framing organizes the work around deciding whether ML is appropriate, outlining the ML problem, selecting a model, and defining success metrics (Google for Developers, 2025).
If the case for ML is not strong enough, document the simpler approach and what evidence could change the decision—for example, reliable labels becoming available or a rule-based process failing to meet a defined outcome. That leaves a clear path to revisit the choice without building a model simply because prediction is possible.
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