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Aryanto
Technical Deep-Dive

The Engineering Behind
the Results.

Anyone can train a model. This page shows the architecture decisions, quality standards, and failure prevention strategies that separate research projects from production systems.

Core Architecture Patterns
3
Reusable across projects
Quality Standard Categories
4
Code, Data, Model, Production
Failure Modes Prevented
5
Proactive, not reactive
Untested Deployments
0
Every model has a test suite

Real-Time Feature Store

When your model needs low-latency access to fresh features at inference time

A centralized feature store serves precomputed and real-time features to models in production. I design feature pipelines that process batch and streaming data into a unified store, ensuring training-serving consistency and eliminating feature drift.

Architecture Components

Batch Pipeline

Airflow + Polars/DuckDB

Historical feature computation

Streaming Pipeline

Kafka / Pub/Sub + Flink

Real-time feature updates

Feature Store

Redis / Bigtable / Feast

Low-latency feature serving

Registry

MLflow / custom

Feature versioning and lineage

Anti-Patterns I Avoid

  • ✗ Computing features in the model serving path (adds latency, creates inconsistency)
  • ✗ Using different code for training vs serving features (train-serve skew)
  • ✗ Storing features in the model artifact (bloats model, prevents reuse)

Automated ML Pipeline

When you need reproducible, auditable model training and deployment

An end-to-end pipeline that automates data validation, feature engineering, model training, evaluation, and deployment — with human approval gates where needed. Every run is logged, every artifact is versioned, every decision is traceable.

Architecture Components

Orchestrator

Airflow / Vertex AI Pipelines

Pipeline scheduling and dependency management

Data Validation

Great Expectations / Pandera

Schema and quality checks before training

Experiment Tracking

MLflow / W&B

Hyperparameter and metric logging

Model Registry

MLflow / Vertex AI Model Registry

Versioned model artifacts with stage transitions

Anti-Patterns I Avoid

  • ✗ Training in notebooks and manually copying artifacts to production
  • ✗ No data validation before training (garbage in, garbage out)
  • ✗ Deploying models without a rollback strategy

ML A/B Testing Framework

When you need to prove a model actually improves business metrics

A statistical framework for comparing model variants in production with proper randomization, sample size calculation, and significance testing. I build systems that answer "is this model actually better?" with math, not vibes.

Architecture Components

Traffic Router

Custom / Envoy

Deterministic user assignment to variants

Metrics Pipeline

BigQuery + dbt

Business metric aggregation per variant

Statistical Engine

Python (scipy, statsmodels)

Bayesian and frequentist significance testing

Dashboard

Looker / Streamlit

Real-time experiment monitoring

Anti-Patterns I Avoid

  • ✗ Peeking at results before reaching sample size (false positives)
  • ✗ Using accuracy as the only metric (optimize for business KPIs)
  • ✗ Running overlapping experiments without interaction analysis

Quality Standards

Non-negotiable standards I apply to every project. These aren't aspirations — they're requirements.

Code Quality

  • Type hints on all function signatures (Python)
  • Docstrings on all public functions and classes
  • Unit tests for data transformations and model logic
  • Linting (ruff) and formatting (black) enforced in CI
  • Code review before merge — no exceptions

Data Quality

  • Schema validation on every data ingestion point
  • Null/outlier monitoring with automated alerts
  • Data lineage tracking from source to feature
  • Freshness SLAs on all input data sources
  • Statistical distribution monitoring for drift detection

Model Quality

  • Holdout test set never seen during development
  • Business metric evaluation alongside ML metrics
  • Fairness and bias evaluation across protected groups
  • Latency benchmarks at expected production throughput
  • Model card documenting assumptions and limitations

Production Quality

  • CI/CD pipeline with automated testing and deployment
  • Health checks and readiness probes on all services
  • Monitoring dashboards with custom alerting rules
  • Rollback procedure tested and documented
  • Runbook for common operational scenarios

Failure Modes I Prevent

The most dangerous failures in ML are silent. These are the ones I design against from day one.

Data Drift

Input data distribution shifts silently, degrading model performance over time.

Prevention Strategy

Automated distribution monitoring with KL-divergence / PSI thresholds. Alerts trigger before business metrics are affected. Automated retraining pipeline kicks in when drift exceeds tolerance.

Concept Drift

The relationship between features and target changes (e.g., post-COVID behavior shifts).

Prevention Strategy

Rolling-window evaluation on recent data. Model performance segmented by time period. Decay-weighted training that emphasizes recent patterns. Human review triggers when segmented performance diverges.

Training-Serving Skew

Features computed differently during training vs. serving, causing silent accuracy loss.

Prevention Strategy

Single feature computation codebase shared between training and serving. Feature store with point-in-time correctness. Integration tests that validate feature parity.

Feedback Loops

Model predictions influence the data it's trained on, creating self-reinforcing bias.

Prevention Strategy

Exploration traffic reserved in recommendation systems. Counterfactual evaluation methods. Periodic randomization to break feedback loops. Explicit logging of which predictions were shown vs. clicked.

Cascade Failures

Upstream data pipeline failure causes model to serve stale or missing features.

Prevention Strategy

Feature freshness monitoring with staleness thresholds. Graceful degradation to default/fallback features. Circuit breaker pattern on feature store dependencies. End-to-end pipeline health dashboard.

Engineering Philosophy

How I make technical decisions when there's no obvious right answer.

Boring Technology

I default to proven, well-documented tools. PostgreSQL over a trendy graph database. XGBoost over a transformer when tabular data is the input. New technology is adopted when the problem demands it — not for the resume line.

Dan McKinley's "Choose Boring Technology"

Reversibility

Every architectural decision is evaluated on reversibility. Highly reversible decisions (library choice, dashboard tool) get fast, lightweight evaluation. Irreversible decisions (data model, cloud provider) get deep analysis and stakeholder alignment.

Jeff Bezos's Type 1/Type 2 framework

Observability Over Testing

You can't test for every edge case in production. You can observe. I build systems with rich metrics, structured logging, and distributed tracing — so when something unexpected happens, I can diagnose it in minutes, not days.

Charity Majors's observability-first approach

Technical Questions

Honest answers about methodology, tradeoffs, and engineering decisions.

Why do you dedicate a whole page to methodology?

Because anyone can claim they "do ML." This page shows the engineering rigor behind my work. If you're evaluating me against other consultants or agencies, this is the differentiator — not buzzwords, but specific architectural decisions and quality standards.

Do you follow these patterns for every project?

Not every project needs every pattern. A proof-of-concept doesn't need a full feature store. A small-scale model doesn't need multi-region deployment. I scale the engineering rigor to match the project requirements — but I never skip the fundamentals (testing, monitoring, documentation).

What's your approach to technical debt?

I'm pragmatic. Some technical debt is intentional (shipping a quick MVP, then iterating). What I won't do is ship untested code, skip monitoring, or pretend a notebook is production-ready. The debt I take on is documented and prioritized for paydown.

How do you handle disagreements on technical approach?

I present my recommendation with reasoning and tradeoffs. If the client wants a different approach, I'll explain the implications honestly — then execute their preferred approach with full commitment. I'm an advisor, not a dictator.

Want to See This in Action?

Theory is nice. Results are better. Check out the case studies to see how these patterns delivered measurable business impact.

View Case Studies → Book a Discovery Call