Afrexai Ml Engineering

# ML & AI Engineering System

Complete methodology for building, deploying, and operating production ML/AI systems — from experiment to scale.

---

## Phase 1: Problem Framing

Before writing any code, define the ML problem precisely.

### ML Problem Brief

```yaml
problem_brief:
  business_objective: ""          # What business metric improves?
  success_metric: ""              # Quantified target (e.g., "reduce churn 15%")
  baseline: ""                    # Current performance without ML
  ml_task_type: ""                # classification | regression | ranking | generation | clustering | anomaly_detection | recommendation
  prediction_target: ""           # What exactly are we predicting?
  prediction_consumer: ""         # Who/what uses the prediction? (API | dashboard | email | automated action)
  latency_requirement: ""         # real-time (<100ms) | near-real-time (<1s) | batch (minutes-hours)
  data_available: ""              # What data exists today?
  data_gaps: ""                   # What's missing?
  ethical_considerations: ""      # Bias risks, fairness requirements, privacy
  kill_criteria:                  # When to abandon the ML approach
    - "Baseline heuristic achieves >90% of ML performance"
    - "Data quality too poor after 2 weeks of cleaning"
    - "Model can't beat random by >10% on holdout set"
```

### ML vs Rules Decision

| Signal | Use Rules | Use ML |
|--------|-----------|--------|
| Logic is explainable in <10 rules | ✅ | ❌ |
| Pattern is too complex for humans | ❌ | ✅ |
| Training data >1,000 labeled examples | — | ✅ |
| Needs to adapt to new patterns | ❌ | ✅ |
| Must be 100% auditable/deterministic | ✅ | ❌ |
| Pattern changes faster than you can update rules | ❌ | ✅ |

**Rule of thumb:** Start with rules/heuristics. Only add ML when rules fail to capture the pattern.

---

## Phase 2: Data Engineering for ML

### Data Quality Assessment

Score each data source (0-5 per dimension):

| Dimension | 0 (Terrible) | 5 (Excel...

# ML & AI Engineering System by AfrexAI ⚡

Complete methodology for building, deploying, and operating production ML/AI systems — from problem framing to monitoring at scale.

## What This Does

Gives your AI agent a complete ML engineering playbook:

- **Problem framing** — ML vs rules decision, problem brief template, kill criteria
- **Data engineering** — Quality scoring, feature engineering patterns, leakage prevention
- **Experiment management** — Model selection guide, hyperparameter tuning, tracking templates
- **Model evaluation** — Metric selection by task, evaluation rigor checklist, offline-to-online gap analysis
- **Deployment** — Pattern decision tree, serving config, A/B testing for models
- **LLM engineering** — RAG architecture, model selection, cost optimization strategies
- **Monitoring** — Drift detection, automated retraining pipeline, response playbooks
- **MLOps** — CI/CD for ML, model registry workflow, platform components
- **Responsible AI** — Bias detection, model cards, fairness metrics
- **Cost optimization** — GPU selection, inference optimization, cost tracking

## Install

```bash
clawhub install afrexai-ml-engineering
```

## Quick Start

Tell your agent: "Frame an ML problem for predicting customer churn" — it will use the problem brief template, help you decide ML vs rules, and guide you through data assessment.

## Example Commands

- "Frame ML problem" — structured problem definition
- "Build RAG system" — full retrieval-augmented generation architecture
- "Deploy model" — serving config with autoscaling
- "Set up monitoring" — drift detection + auto-retraining
- "Score ML system" — 100-point quality rubric

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