Entry / SDE I / entry ML
A coherent simple end-to-end design: formulation, data, labels, one metric, leakage avoidance, one serving path, and one fallback.
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Self-review
Use evidence in each dimension to decide what to improve next. The rubric does not calculate a readiness score.
Read each row independently. A design can be strong in data and weak in rollout; averaging those signals would hide the next useful action.
| Dimension | Needs development | Acceptable | Strong | Exceptional |
|---|---|---|---|---|
| Problem framing | Jumps to a model; decision unclear | Defines user, task, and basic constraints | Connects decision, ML task, and baseline | Challenges whether ML is needed and anticipates stakeholders and evolution |
| Data and labels | Says only collect data | Names plausible sources and labels | Handles exposure, delay, noise, availability, and leakage | Designs label operations, lineage, missing counterfactuals, and repair |
| Metrics | Uses a generic metric | Chooses a task-appropriate offline metric | Connects product, task, retrieval, slice, and guardrail measures | Explains proxy conflict, causal limits, long-term effects, and capacity |
| Architecture | Draws boxes without flow | Shows coherent train and serve paths | Makes state, timing, version, and dependency boundaries clear | Compares architectures and migration or evolution paths |
| ML judgment | Dumps model names | Chooses a reasonable model family | Justifies complexity from data, constraints, and baseline | Finds non-model bottlenecks and defines evidence-driven escalation |
| Production engineering | Ignores serving and deployment | Provides a basic service and pipeline | Covers freshness, rollout, fallback, monitoring, and cost | Handles multi-region or tenant concerns, incidents, migration, and ownership |
| Experimentation | Says only A/B test it | Defines treatment and control | Covers integrity, guardrails, ramp, and rollback | Handles interference, long-term effects, safety, and decision limits |
| Reliability and evolution | Shows only the happy path | Names one fallback and monitoring plan | Uses multi-layer monitoring, diagnosis, and retraining policy | Covers feedback, abuse, compatibility, and operational governance |
| Risk and responsibility | Adds a generic disclaimer | Names a relevant risk | Ties harm to controls, review, and appeal | Anticipates conflicting risks, auditability, and policy evolution |
| Communication | Lists components without structure | Presents an understandable sequence | Keeps assumptions and trade-offs visible | Adapts depth, handles challenge, and closes with decisions |
A coherent simple end-to-end design: formulation, data, labels, one metric, leakage avoidance, one serving path, and one fallback.
Point-in-time correctness, batch/stream decisions, deployment, monitoring, cost, delayed labels, alternatives, and failure handling.
Ambiguous framing, boundaries, migration, regional or tenant concerns where relevant, ownership, long-term effects, auditability, and cost-risk trades.
High production depth across training, feature, serving, deployment, monitoring, feedback, and reliability while retaining correct formulation.
Balanced depth across the complete data, model, infrastructure, experimentation, and monitoring lifecycle.
Stronger formulation, modeling judgment, metrics, experimentation, and uncertainty with enough production detail to remain realistic.