Rules would suffice but ML is forced—teams then argue across ownership lines.
Evaluable ML
Machine Learning Development
ML without offline metrics turns into post-launch arguments. Clarify the boundary before choosing the build path.
From problem/data to evaluable models and inference wiring.
Compliant crawling
We review target terms and legal boundaries first.
Reproducible envs
Locked deps and run notes—not 'works on my machine'.
Acceptable results
Samples and metrics define done—not vague 'looks ok'.
ML failure modes
These usually show up before a project starts—or right after a rushed launch.
Leakage inflates scores—it often surfaces only after production impact.
No baseline—iteration and local integration slow down.
Train/serve feature skew—users feel it as inconsistent data or UX.
Metrics and baselines first
Define success metrics; strong baselines; leakage checks; reproducible features; small-traffic validation. Confirm ML is needed. If yes, lock metrics, data quality and baselines—small experiments, no demo-only models.
Confirm ML is needed. If yes, lock metrics, data quality and baselines—small experiments, no demo-only models.
- Scope written before coding
- Milestones you can accept
- Handover notes included
Highlights
What this engagement typically covers.
Problem & metrics
Included in scope after we confirm stack, constraints and acceptance checks.
Data assessment
Included in scope after we confirm stack, constraints and acceptance checks.
Model experiments
Included in scope after we confirm stack, constraints and acceptance checks.
Inference wiring
Included in scope after we confirm stack, constraints and acceptance checks.
What you get
- Data assessment
- Experiment report
- Model artifact
- Inference API
- Monitoring tips
How we work
-
01
Problem define, with written stage outputs.
-
02
Data assess, with written stage outputs.
-
03
Experiment, with written stage outputs.
-
04
Wire pilot, with written stage outputs.
Ready to lock scope?
Share the metric and data volume—we'll judge feasibility.