AI & Machine Learning Engineering

Design, train, optimize, deploy, and monitor production machine learning models and intelligent agent systems with rigorous validation, latency engineering, and ethical guardrails.

Core Principle

Every ML solution must beat a naive baseline, guarantee train-test isolation, and meet strict production latency and fairness budgets.

Core Invariants

1. Strict Train-Test Isolation: Preprocessing scalers, encoders, and tokenizers must be fit strictly on training folds inside isolated pipelines to eliminate data leakage.
2. Sub-100ms Inference Budget: Synchronous user-facing endpoints must achieve $p99 < 80\text{ms}$ through quantization (ONNX/TensorRT/GGUF), engine optimization, or continuous batching.
3. Mandatory Baseline Before Complexity: Always benchmark against a naive baseline (majority class/mean) and simple linear model before advancing to deep networks or complex ensembles.
4. Demographic Parity & Bias Auditing: Every production candidate must pass four-fifths disparate impact testing across demographic slices ($\text{ratio} \ge 0.80$).
5. Continuous Drift Observability: Production models must emit data drift metrics (KS-test / Population Stability Index) and concept drift alerts to trigger automated retraining.

Architecture & Map of Content (MOC)

Problem Framing & Data Assessment
              │
              ▼
┌───────────────────────────────────────┐
│ 1. Data Prep & Leakage-Free Pipeline  │ (Cross-Validation, Feature Isolation)
└──────────────────┬────────────────────┘
                   │
                   ▼
┌───────────────────────────────────────┐
│ 2. Model Training & Fine-Tuning       │ (Baselines, LoRA / QLoRA, Tree Ensembles)
└──────────────────┬────────────────────┘
                   │
                   ▼
┌───────────────────────────────────────┐
│ 3. Bias, Fairness & Ethics Audit      │ (Disparate Impact, SHAP Attributions)
└──────────────────┬────────────────────┘
                   │
                   ▼
┌───────────────────────────────────────┐
│ 4. Production Serving & Optimization  │ (ONNX/vLLM, Sub-100ms Latency, Canary)
└───────────────────────────────────────┘

Step-by-Step Procedure (TWI)

Step 1: Requirements & Data Hygiene Verification

• Action: Assess target metrics, evaluate class balance, and establish reproducible validation splits.
• Key Point: Check for temporal dependencies. If data has a time dimension, use chronological splitting; otherwise, use Stratified $k$-Fold cross-validation.
• Why: Random splits on time-series data cause severe lookahead leakage and false confidence.

Step 2: Model Training & Architecture Selection

• Action: Fit naive baselines, progress to gradient-boosted trees or fine-tune neural architectures (LoRA/PEFT for LLMs).
• Key Point: For LLMs, apply LoRA to all attention and MLP projection layers with $\alpha = 2 \times r$ and 4-bit NF4 quantization.
• Why: Selective rank adaptation achieves foundation model performance at 25% of the VRAM cost.
• Inline Checklist:
  • Simple baseline established (Linear/Logistic Regression or Mean baseline)
  • Cross-validation variance across folds is within acceptable bounds (< 5%)
  • Overfitting diagnostics checked (Training loss vs Validation loss convergence)
  • Hyperparameters tuned systematically using Bayesian optimization

Step 3: Bias, Explainability & Robustness Auditing

• Action: Evaluate fairness across subpopulations and calculate SHAP feature attributions.
• Key Point: Enforce the four-fifths rule ($\text{selection rate ratio} \ge 0.80$) and compute top local feature attributions for high-stakes decisions.
• Why: Undetected bias causes regulatory violations and uncalibrated real-world discrimination.

Step 4: Low-Latency Serving & Canary Rollout

• Action: Export models to ONNX or vLLM, wrap in asynchronous API services, and initiate a 10% canary deployment.
• Key Point: Track $p99$ latency, Population Stability Index (PSI), and error rates across the canary group.
• Why: Canary testing isolates performance regressions before full traffic exposure.

Anti-Rationalization Guardrails