Mle Workflow

Machine Learning Engineering Workflow

Use this skill to turn model work into a production ML system with clear data contracts, repeatable training, measurable quality gates, deployable artifacts, and operational monitoring.

When to Activate

• Planning or reviewing a production ML feature, model refresh, ranking system, recommender, classifier, embedding workflow, or forecasting pipeline
• Converting notebook code into a reusable training, evaluation, batch inference, or online inference pipeline
• Designing model promotion criteria, offline/online evals, experiment tracking, or rollback paths
• Debugging failures caused by data drift, label leakage, stale features, artifact mismatch, or inconsistent training and serving logic
• Adding model monitoring, canary rollout, shadow traffic, or post-deploy quality checks

Scope Calibration

Use only the lanes that fit the system in front of you. This skill is useful for ranking, search, recommendations, classifiers, forecasting, embeddings, LLM workflows, anomaly detection, and batch analytics, but it should not force one architecture onto all of them.

• Do not assume every model has supervised labels, online serving, a feature store, PyTorch, GPUs, human review, A/B tests, or real-time feedback.
• Do not add heavyweight MLOps machinery when a data contract, baseline, eval script, and rollback note would make the change reviewable.
• Do make assumptions explicit when the project lacks labels, delayed outcomes, slice definitions, production traffic, or monitoring ownership.
• Treat examples as interchangeable scaffolds. Replace metrics, serving mode, data stores, and rollout mechanics with the project-native equivalents.

Related Skills

• python-patterns and python-testing for Python implementation and pytest coverage
• pytorch-patterns for deep learning models, data loaders, device handling, and training loops
• eval-harness and ai-regression-testing for promotion gates and agent-assisted regression checks
• database-migrations, postgres-patterns, and clickhouse-io for data storage and analytics surfaces
• deployment-patterns, docker-patterns, and security-review for serving, secrets, containers, and production hardening

Reuse the SWE Surface

Do not treat MLE as separate from software engineering. Most ECC SWE workflows apply directly to ML systems, often with stricter failure modes:

The recommended minimal --with capability:machine-learning install keeps the core agent surface available alongside this skill. For skill-only or agent-limited harnesses, pair skill:mle-workflow with agent:mle-reviewer where the target supports agents.

| SWE surface | MLE use | |-------------|---------| | product-capability / architecture-decision-records | Turn model work into explicit product contracts and record irreversible data, model, and rollout choices | | repo-scan / codebase-onboarding / code-tour | Find existing training, feature, serving, eval, and monitoring paths before introducing a parallel ML stack | | plan / feature-dev | Scope model changes as product capabilities with data, eval, serving, and rollback phases | | tdd-workflow / python-testing | Test feature transforms, split logic, metric calculations, artifact loading, and inference schemas before implementation | | code-reviewer / mle-reviewer | Review code quality plus ML-specific leakage, reproducibility, promotion, and monitoring risks | | build-fix / pr-test-analyzer | Diagnose broken CI, flaky evals, missing fixtures, and environment-specific model or dependency failures | | quality-gate / test-coverage | Require automated evidence for transforms, metrics, inference contracts, promotion gates, and rollback behavior | | eval-harness / verification-loop | Turn offline metrics, slice checks, latency budgets, and rollback drills into repeatable gates | | ai-regression-testing | Preserve every production bug as a regression: missing feature, stale label, bad artifact, schema drift, or serving mismatch | | api-design / backend-patterns | Design prediction APIs, batch jobs, idempotent retraining endpoints, and response envelopes | | database-migrations / postgres-patterns / clickhouse-io | Version labels, feature snapshots, prediction logs, experiment metrics, and drift analytics | | deployment-patterns / docker-patterns | Package reproducible training and serving images with health checks, resource limits, and rollback | | canary-watch / dashboard-builder | Make rollout health visible with model-version, slice, drift, latency, cost, and delayed-label dashboards | | security-review / security-scan | Check model artifacts, notebooks, prompts, datasets, and logs for secrets, PII, unsafe deserialization, and supply-chain risk | | e2e-testing / browser-qa / accessibility | Test critical product flows that consume predictions, including explainability and fallback UI states | | benchmark / performance-optimizer | Measure throughput, p95 latency, memory, GPU utilization, and cost per prediction or retrain | | cost-aware-llm-pipeline / token-budget-advisor | Route LLM/embedding workloads by quality, latency, and budget instead of defaulting to the largest model | | documentation-lookup / search-first | Verify current library behavior for model serving, feature stores, vector DBs, and eval tooling before coding | | git-workflow / github-ops / opensource-pipeline | Package MLE changes for review with crisp scope, generated artifacts excluded, and reproducible test evidence | | strategic-compact / dmux-workflows | Split long ML work into parallel tracks: data contract, eval harness, serving path, monitoring, and docs |

Ten MLE Task Simulations

Use these simulations as coverage checks when planning or reviewing MLE work. A strong MLE workflow should reduce each task to explicit contracts, reusable SWE surfaces, automated evidence, and a reviewable artifact.

| ID | Common MLE task | Streamlined ECC path | Required output | Pipeline lanes covered | |----|-----------------|----------------------|-----------------|------------------------| | MLE-01 | Frame an ambiguous prediction, ranking, recommender, classifier, embedding, or forecast capability | product-capability, plan, architecture-decision-records, mle-workflow | Iteration Compact naming who cares, decision owner, success metric, unacceptable mistakes, assumptions, constraints, and first experiment | product contract, stakeholder loss, risk, rollout | | MLE-02 | Define metric goals, labels, data sources, and the mistake budget | repo-scan, database-reviewer, database-migrations, postgres-patterns, clickhouse-io | Data and metric contract with entity grain, label timing, label confidence, feature timing, point-in-time joins, split policy, and dataset snapshot | data contract, metric design, leakage, reproducibility | | MLE-03 | Build a baseline model and scoring path before adding complexity | tdd-workflow, python-testing, python-patterns, code-reviewer | Baseline scorer with confusion matrix, calibration notes, latency/cost estimate, known weaknesses, and tests for score shape and determinism | baseline, scoring, testing, serving parity | | MLE-04 | Generate features from hypotheses about what separates outcomes | python-patterns, pytorch-patterns, docker-patterns, deployment-patterns | Feature plan and transform module covering signal source, missing values, outliers, correlations, leakage checks, and train/serve equivalence | feature pipeline, leakage, training, artifacts | | MLE-05 | Tune thresholds, configs, and model complexity under tradeoffs | eval-harness, ai-regression-testing, quality-gate, test-coverage | Threshold/config report comparing precision, recall, F1, AUC, calibration, group slices, latency, cost, complexity, and acceptable error classes | evaluation, threshold, promotion, regression | | MLE-06 | Run error analysis and turn mistakes into the next experiment | eval-harness, ai-regression-testing, mle-reviewer, silent-failure-hunter | Error cluster report for false positives, false negatives, ambiguous labels, stale features, missing signals, and bug traces with lessons captured | error analysis, bug trace, iteration, regression | | MLE-07 | Package a model artifact for batch or online inference | api-design, backend-patterns, security-review, security-scan | Versioned artifact bundle with preprocessing, config, dependency constraints, schema validation, safe loading, and PII-safe logs | artifact, security, inference contract | | MLE-08 | Ship online serving or batch scoring with feedback capture | api-design, backend-patterns, e2e-testing, browser-qa, accessibility | Prediction endpoint or batch job with response envelope, timeout, batching, fallback, model version, confidence, feedback logging, and product-flow tests | serving, batch inference, fallback, user workflow | | MLE-09 | Roll out a model with shadow traffic, canary, A/B test, or rollback | canary-watch, dashboard-builder, verification-loop, performance-optimizer | Rollout plan naming traffic split, dashboards, p95 latency, cost, quality guardrails, rollback artifact, and rollback trigger | deployment, canary, rollback | | MLE-10 | Operate, debug, and refresh a production model after launch | silent-failure-hunter, dashboard-builder, mle-reviewer, doc-updater, github-ops | Observation ledger and refresh plan with drift checks, delayed-label health, alert owners, runbook updates, retrain criteria, and PR evidence | monitoring, incident response, retraining |

Iteration Compact

Before touching model code, compress the work into one reviewable artifact. This should be short enough to fit in a PR description and precise enough that another engineer can challenge the tradeoffs.

Goal:
Who cares:
Decision owner:
User or system action changed by the model:
Success metric:
Guardrail metrics:
Mistake budget:
Unacceptable mistakes:
Acceptable mistakes:
Assumptions:
Constraints:
Labels and data snapshot:
Baseline:
Candidate signals:
Threshold or config plan:
Eval slices:
Known risks:
Next experiment:
Rollback or fallback:

This compact is the MLE equivalent of a strong SWE design note. It keeps the team from optimizing a metric no one trusts, adding features that do not address the real error mode, or shipping complexity without a rollback.

Decision Brain

Use this loop whenever the task is ambiguous, high-impact, or metric-heavy:

1. Start from the decision, not the model. Name the action that changes downstream behavior.
2. Name who cares and why. Different stakeholders pay different costs for false positives, false negatives, latency, compute spend, opacity, or missed opportunities.
3. Convert ambiguity into hypotheses. Ask what signal would separate outcomes, what evidence would disprove it, and what simple baseline should be hard to beat.
4. Research prior art or a nearby known problem before inventing a bespoke system.
5. Score choices with (probability, confidence) x (cost, severity, importance, impact).
6. Consider adversarial behavior, incentives, selective disclosure, distribution shift, and feedback loops.
7. Prefer the simplest change that reduces the most important mistake. Simplicity is not laziness; it is a way to minimize blunders while preserving iteration speed.
8. Capture the decision, evidence, counterargument, and next reversible step.

Metric and Mistake Economics

Choose metrics from failure costs, not habit:

• Use a confusion matrix early so the team can discuss concrete false positives and false negatives instead of abstract accuracy.
• Favor precision when the cost of an incorrect positive decision dominates.
• Favor recall when the cost of a missed positive dominates.
• Use F1 only when the precision/recall tradeoff is genuinely balanced and explainable.
• Use AUC or ranking metrics when ordering quality matters more than a single threshold.
• Track latency, throughput, memory, and cost as first-class metrics because they shape feasible model complexity.
• Compare against a baseline and the current production model before celebrating an offline gain.
• Treat real-world feedback signals as delayed labels with bias, lag, and coverage gaps; do not treat them as ground truth without analysis.

Every metric choice should state which mistake it makes cheaper, which mistake it makes more likely, and who absorbs that cost.

Data and Feature Hypotheses

Features should come from a theory of separation:

• Text, categorical fields, numeric histories, graph relationships, recency, frequency, and aggregates are candidate signal families, not automatic features.
• For every feature family, state why it should separate outcomes and how it could leak future information.
• For noisy labels, consider adjudication, label confidence, soft targets, or confidence weighting.
• For class imbalance, compare weighted loss, resampling, threshold movement, and calibrated decision rules.
• For missing values, decide whether absence is informative, imputable, or a reason to abstain.
• For outliers, decide whether to clip, bucket, investigate, or preserve them as rare but important signal.
• For correlated features, check whether they are redundant, unstable, or proxies for unavailable future state.

Do not add model complexity until error analysis shows that the baseline is failing for a reason additional signal or capacity can plausibly fix.

Error Analysis Loop

After each baseline, training run, threshold change, or config change:

1. Split mistakes into false positives, false negatives, abstentions, low-confidence cases, and system failures.
2. Cluster errors by shared traits: language, entity type, source, time, geography, device, sparsity, recency, feature freshness, label source, or model version.
3. Separate model mistakes from data bugs, label ambiguity, product ambiguity, instrumentation gaps, and serving mismatches.
4. Trace each major cluster to one of four moves: better labels, better features, better threshold/config, or better product fallback.
5. Preserve every important mistake as a regression test, eval slice, dashboard panel, or runbook entry.
6. Write the next iteration as a falsifiable experiment, not a vague "improve model" task.

The strongest MLE loop is not train -> metric -> ship. It is mistake -> cluster -> hypothesis -> experiment -> evidence -> simpler system.

Observation Ledger

Keep a compact decision and evidence trail beside the code, PR, experiment report, or runbook:

Iteration:
Change:
Why this mattered:
Metric movement:
Slice movement:
False positives:
False negatives:
Unexpected errors:
Decision:
Tradeoff accepted:
Lesson captured:
Regression added:
Debt created:
Next iteration:

Use the ledger to make model work cumulative. The goal is for each iteration to make the next decision easier, not merely to produce another artifact.

Core Workflow

1. Define the Prediction Contract

Capture the product-level contract before writing model code:

• Prediction target and decision owner
• Input entity, output schema, confidence/calibration fields, and allowed latency
• Batch, online, streaming, or hybrid serving mode
• Fallback behavior when the model, feature store, or dependency is unavailable
• Human review or override path for high-impact decisions
• Privacy, retention, and audit requirements for inputs, predictions, and labels

Do not accept "improve the model" as a requirement. Tie the model to an observable product behavior and a measurable acceptance gate.

2. Lock the Data Contract

Every ML task needs an explicit data contract:

• Entity grain and primary key
• Label definition, label timestamp, and label availability delay
• Feature timestamp, freshness SLA, and point-in-time join rules
• Train, validation, test, and backtest split policy
• Required columns, allowed null

…

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: affaan-m
• Source: affaan-m/ECC
• License: MIT
• Homepage: https://ecc.tools

Install and usage instructions live in the source repository linked above.