ML & AI Engineering System

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

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

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):

Minimum score to proceed: 18/30. Below 18 → fix data first, don't build models.

Feature Engineering Patterns

feature_types:
  numerical:
    - raw_value           # Use as-is if normally distributed
    - log_transform       # Right-skewed distributions (revenue, counts)
    - standardize         # z-score for algorithms sensitive to scale (SVM, KNN, neural nets)
    - bin_to_categorical  # When relationship is non-linear and data is limited
  categorical:
    - one_hot             # <20 categories, tree-based models handle natively
    - target_encoding     # High-cardinality (>20 categories), use with K-fold to prevent leakage
    - embedding           # Very high-cardinality (user IDs, product IDs) with deep learning
  temporal:
    - lag_features        # Value at t-1, t-7, t-30
    - rolling_statistics  # Mean, std, min, max over windows
    - time_since_event    # Days since last purchase, hours since login
    - cyclical_encoding   # sin/cos for hour-of-day, day-of-week, month
  text:
    - tfidf               # Simple, interpretable, good baseline
    - sentence_embeddings # semantic similarity, modern NLP
    - llm_extraction      # Use LLM to extract structured fields from unstructured text
  interaction:
    - ratios              # Feature A / Feature B (e.g., clicks/impressions = CTR)
    - differences         # Feature A - Feature B (e.g., price - competitor_price)
    - polynomial          # A * B, A^2 (use sparingly, high-cardinality features)

Feature Store Design

feature_store:
  offline_store:         # For training — batch computed, stored in data warehouse
    storage: "BigQuery | Snowflake | S3+Parquet"
    compute: "Spark | dbt | SQL"
    refresh: "daily | hourly"
  online_store:          # For serving — low-latency lookups
    storage: "Redis | DynamoDB | Feast online"
    latency_target: "<10ms p99"
    refresh: "streaming | near-real-time"
  registry:              # Feature metadata
    naming: "{entity}_{feature_name}_{window}_{aggregation}"  # e.g., user_purchase_count_30d_sum
    documentation:
      - description
      - data_type
      - source_table
      - owner
      - created_date
      - known_issues

Data Leakage Prevention Checklist

• No future information in features (time-travel check)
• Train/val/test split done BEFORE feature engineering
• Target encoding uses only training fold statistics
• No features derived from the target variable
• Temporal splits for time-series (no random shuffle)
• Holdout set created BEFORE any EDA
• Duplicates removed BEFORE splitting (same entity not in train AND test)
• Normalization/scaling fit on train, applied to val/test

Phase 3: Experiment Management

Experiment Tracking Template

experiment:
  id: "EXP-{YYYY-MM-DD}-{NNN}"
  hypothesis: ""                 # "Adding user tenure features will improve churn prediction AUC by >2%"
  dataset_version: ""            # Hash or version of training data
  features_used: []              # List of feature names
  model_type: ""                 # Algorithm name
  hyperparameters: {}            # All hyperparams logged
  training_time: ""              # Wall clock
  metrics:
    primary: {}                  # The one metric that matters
    secondary: {}                # Supporting metrics
  baseline_comparison: ""        # Delta vs baseline
  verdict: "promoted | archived | iterate"
  notes: ""
  artifacts:
    - model_path: ""
    - notebook_path: ""
    - confusion_matrix: ""

Model Selection Guide

Hyperparameter Tuning Strategy

1. Manual first — understand 3-5 most impactful parameters
2. Bayesian optimization (Optuna) — 50-100 trials for production models
3. Grid search — only for final fine-tuning of 2-3 parameters
4. Random search — better than grid for >4 parameters

Key hyperparameters by model:

Phase 4: Model Evaluation

Metric Selection by Task

Evaluation Rigor Checklist

• Metrics computed on TRUE holdout (never seen during training OR tuning)
• Cross-validation for small datasets (<10K samples)
• Stratified splits for imbalanced classes
• Temporal split for time-dependent data
• Confidence intervals reported (bootstrap or cross-val)
• Performance broken down by important segments (geography, user cohort, etc.)
• Fairness metrics across protected groups
• Comparison against simple baseline (majority class, mean prediction, rules)
• Error analysis: examined top 50 worst predictions manually
• Calibration plot for probabilistic predictions

Offline-to-Online Gap Analysis

Before deploying, verify these don't cause train-serving skew:

Phase 5: Model Deployment

Deployment Pattern Decision Tree

Is latency < 100ms required?
├── Yes → Is model < 500MB?
│   ├── Yes → Embedded serving (FastAPI + model in memory)
│   └── No → Model server (Triton, TorchServe, vLLM)
└── No → Is it a batch prediction?
    ├── Yes → Batch pipeline (Spark, Airflow + offline inference)
    └── No → Async queue (Celery/SQS → worker → result store)

Production Serving Checklist

serving_config:
  model:
    format: ""                    # ONNX | TorchScript | SavedModel | safetensors
    version: ""                   # Semantic version
    size_mb: null
    load_time_seconds: null
  infrastructure:
    compute: ""                   # CPU | GPU (T4/A10/A100/H100)
    instances: null               # Min/max for autoscaling
    autoscale_metric: ""          # RPS | latency_p99 | GPU_utilization
    autoscale_target: null
  api:
    endpoint: ""
    input_schema: {}              # Pydantic model or JSON schema
    output_schema: {}
    timeout_ms: null
    rate_limit: null
  reliability:
    health_check: "/health"
    readiness_check: "/ready"     # Model loaded and warm
    graceful_shutdown: true
    circuit_breaker: true
    fallback: ""                  # Rules-based fallback when model is down

Containerization Template

# Multi-stage build for minimal image
FROM python:3.11-slim AS builder
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

FROM python:3.11-slim
WORKDIR /app
COPY --from=builder /usr/local/lib/python3.11/site-packages /usr/local/lib/python3.11/site-packages
COPY --from=builder /usr/local/bin /usr/local/bin
COPY model/ ./model/
COPY src/ ./src/

# Non-root user
RUN useradd -m appuser && chown -R appuser /app
USER appuser

# Health check
HEALTHCHECK --interval=30s --timeout=5s CMD curl -f http://localhost:8080/health || exit 1

EXPOSE 8080
CMD ["uvicorn", "src.serve:app", "--host", "0.0.0.0", "--port", "8080"]

A/B Testing for Models

ab_test:
  name: ""
  hypothesis: ""
  primary_metric: ""              # Business metric (revenue, engagement, etc.)
  guardrail_metrics: []           # Metrics that must NOT degrade
  traffic_split:
    control: 50                   # Current model
    treatment: 50                 # New model
  minimum_sample_size: null       # Power analysis: use statsmodels or online calculator
  minimum_runtime_days: null      # At least 1 full business cycle (7 days min)
  decision_criteria:
    ship: "Treatment > control by >X% with p<0.05 AND no guardrail regression"
    iterate: "Promising signal but not significant — extend test or refine model"
    kill: "No improvement after 2x minimum runtime OR guardrail breach"

Phase 6: LLM Engineering

LLM Application Architecture

┌─────────────────────────────────────────────┐
│              Application Layer               │
│  (Prompt templates, chains, output parsers)  │
├─────────────────────────────────────────────┤
│              Orchestration Layer              │
│  (Routing, fallback, retry, caching)         │
├─────────────────────────────────────────────┤
│              Model Layer                     │
│  (API calls, fine-tuned models, embeddings)  │
├─────────────────────────────────────────────┤
│              Data Layer                      │
│  (Vector store, context retrieval, memory)   │
└─────────────────────────────────────────────┘

Model Selection for LLM Tasks

RAG System Architecture

rag_pipeline:
  ingestion:
    chunking:
      strategy: "semantic"         # semantic | fixed_size | recursive
      chunk_size: 512              # tokens (512-1024 for most use cases)
      overlap: 50                  # tokens overlap between chunks
      metadata_to_preserve:
        - source_document
        - page_number
        - section_heading
        - date_created
    embedding:
      model: "text-embedding-3-large"
      dimensions: 1536             # Or 256/512 with Matryoshka for cost savings
    vector_store: "Pinecone | Weaviate | pgvector | Qdrant"
  retrieval:
    strategy: "hybrid"             # dense | sparse | hybrid (recommended)
    top_k: 10                      # Retrieve more, then rerank
    reranking:
      model: "Cohere rerank | cross-encoder"
      top_n: 3                     # Final context chunks
    filters: []                    # Metadata filters (date range, source, etc.)
  generation:
    model: ""
    system_prompt: |
      Answer based ONLY on the provided context.
      If the context doesn't contain the answer, say "I don't have enough information."
      Cite sources using [Source: document_name, page X].
    temperature: 0.1               # Low for factual, higher for creative
    max_tokens: null

RAG Quality Checklist

• Chunking preserves semantic meaning (not cutting mid-sentence)
• Metadata enables filtering (dates, sources, categories)
• Retrieval returns relevant chunks (test with 50+ queries manually)
• Reranking improves precision (compare with/without)
• System prompt prevents hallucination (tested with adversarial queries)
• Sources are cited and verifiable
• Handles "I don't know" gracefully
• Latency acceptable (<3s for interactive, <30s for complex)
• Cost per query tracked and within budget

LLM Cost Optimization

Phase 7: Model Monitoring

Monitoring Dashboard

monitoring:
  model_performance:
    metrics:
      - name: "primary_metric"         # Same as offline evaluation
        threshold: null                 # Alert if below
        window: "1h | 1d | 7d"
      - name: "prediction_distribution"
        alert: "KL divergence > 0.1 from training distribution"
    latency:
      p50_ms: null
      p95_ms: null
      p99_ms: null
      alert_threshold_ms: null
    throughput:
      requests_per_second: null
      error_rate_threshold: 0.01       # Alert if >1% errors
  data_drift:
    method: "PSI | KS-test | JS-divergence"
    features_to_monitor: []            # Top 10 most important features
    check_frequency: "hourly | daily"
    alert_threshold: null              # PSI > 0.2 = significant drift
  concept_drift:
    method: "performance_degradation"
    ground_truth_delay: ""             # How long until we get labels?
    proxy_metrics: []                  # Metrics available before ground truth
    retraining_trigger: ""             # When to retrain

Drift Response Playbook

Automated Retraining Pipeline

retraining:
  triggers:
    - type: "scheduled"
      frequency: "weekly | monthly"
    - type: "performance"
      condition: "primary_metric < threshold for 24h"
    - type: "drift"
      condition: "PSI > 0.2 on any top-10 feature"
  pipeline:
    1_data_validation:
      - check_completeness
      - check_distribution_shift
      - check_label_quality
    2_training:
      - use_latest_N_months_data
      - same_hyperparameters_as_production   # Unless scheduled tuning
      - log_all_metrics
    3_evaluation:
      - compare_vs_production_model
      - must_beat_production_on_primary_metric
      - must_not_regress_on_guardrail_metrics
      - evaluate_on_golden_test_set
    4_deployment:
      - canary_deployment: 5%
      - monitor_for: "4h minimum"
      - auto_rollback_if: "error_rate > 2x baseline"
      - gradual_rollout: "5% → 25% → 50% → 100%"
    5_notification:
      - log_retraining_event
      - notify_team_on_failure
      - update_model_registry

Phase 8: MLOps Infrastructure

ML Platform Components

CI/CD for ML

ml_cicd:
  on_code_change:
    - lint_and_type_check
    - unit_tests (data transforms, feature logic)
    - integration_tests (pipeline end-to-end on sample data)
  on_data_change:
    - data_validation (Great Expectations / custom)
    - feature_pipeline_run
    - smoke_test_predictions
  on_model_change:
    - full_evaluation_suite
    - bias_and_fairness_check
    - performance_regression_test
    - model_size_and_latency_check
    - security_scan (model file, dependencies)
    - staging_deployment
    - integration_test_in_staging
    - approval_gate (manual for major versions)
    - canary_deployment

Model Registry Workflow

┌──────────────┐      ┌──────────────┐      ┌──────────────┐
│  Development │ ───→ │   Staging    │ ───→ │  Production  │
│              │      │              │      │              │
│ - Experiment │      │ - Eval suite │      │ - Canary     │
│ - Log metrics│      │ - Load test  │      │ - Monitor    │
│ - Compare    │      │ - Approval   │      │ - Rollback   │
└──────────────┘      └──────────────┘      └──────────────┘

Promotion criteria:

• Dev → Staging: Beats current production on offline metrics
• Staging → Production: Passes load test + integration test + human approval
• Auto-rollback: Error rate >2x OR latency >2x OR primary metric drops >5%

Phase 9: Responsible AI

Bias Detection Checklist

• Training data represents all demographic groups proportionally
• Performance metrics broken down by protected attributes
• Equal opportunity: similar true positive rates across groups
• Calibration: predicted probabilities match actual rates per group
• No proxy features for protected attributes (ZIP code → race)
• Fairness metric selected and threshold defined BEFORE training
• Disparate impact ratio >0.8 (80% rule)
• Edge cases tested: what happens with unusual inputs?

Model Card Template

model_card:
  model_name: ""
  version: ""
  date: ""
  owner: ""
  description: ""
  intended_use: ""
  out_of_scope_uses: ""
  training_data:
    source: ""
    size: ""
    date_range: ""
    known_biases: ""
  evaluation:
    metrics: {}
    datasets: []
    sliced_metrics: {}             # Performance by subgroup
  limitations: []
  ethical_considerations: []
  maintenance:
    retraining_schedule: ""
    monitoring: ""
    contact: ""

Phase 10: Cost & Performance Optimization

GPU Selection Guide

Inference Optimization Techniques

Cost Tracking Template

ml_costs:
  training:
    compute_cost_per_run: null
    runs_per_month: null
    data_storage_monthly: null
    experiment_tracking: null
  inference:
    cost_per_1k_predictions: null
    daily_volume: null
    monthly_cost: null
    cost_per_query_breakdown:
      compute: null
      model_api_calls: null
      vector_db: null
      data_transfer: null
  optimization_targets:
    cost_per_prediction: null      # Target
    monthly_budget: null
    cost_reduction_goal: ""

Phase 11: ML System Quality Rubric

Score your ML system (0-100):

Scoring:

• 80-100: Production-grade ML system
• 60-79: Good foundations, missing operational maturity
• 40-59: Prototype quality, not ready for production
• <40: Science project, needs fundamental rework

Common Mistakes

Quick Commands

• "Frame ML problem" → Phase 1 brief
• "Assess data quality" → Phase 2 scoring
• "Select model" → Phase 3 guide
• "Evaluate model" → Phase 4 checklist
• "Deploy model" → Phase 5 serving config
• "Build RAG" → Phase 6 RAG architecture
• "Set up monitoring" → Phase 7 dashboard
• "Optimize costs" → Phase 10 tracking
• "Score ML system" → Phase 11 rubric
• "Detect drift" → Phase 7 playbook
• "A/B test model" → Phase 5 template
• "Create model card" → Phase 9 template