AI/ML Engineering

Production Machine Learning Systems That Actually Work

PhD-led ML consulting for companies that need models in production, not just notebooks. From prototype to deployment, we build ML systems that scale.

Start a ProjectMeet the Team
PhD in Machine Learning (UW)
Former Spotify ML (via acquisition)
15+ years production ML

Current Projects

Cultivarium

Building computer vision systems for biotech applications. Image analysis pipelines for biological research at scale.

cultivarium.org →

SkyNewt (Open Source)

Open-source tooling for scaling computer vision workflows on AWS. Simplifying CV deployment and batch processing.

Coming soon on GitHub

Why ML Models Fail in Production

Common Failures

  • ✕Models that work in notebooks but crash in production
  • ✕No monitoring—you don't know when performance degrades
  • ✕Manual deployment processes that break at scale
  • ✕Training-serving skew causing silent failures

What We Build Instead

  • ✓Production-hardened models with proper error handling
  • ✓Real-time monitoring with actionable alerts
  • ✓Automated CI/CD pipelines for ML
  • ✓Feature pipelines that ensure consistency

ML Consulting Capabilities

End-to-end ML engineering, from model development through production deployment and monitoring.

Computer Vision & Image Analysis

Production CV systems for real-world applications. From biotech imaging to industrial inspection, we build vision pipelines that scale.

  • Object detection & segmentation
  • Medical/biotech image analysis
  • Video processing pipelines
  • Edge deployment (AWS, NVIDIA)
  • Custom model fine-tuning

Model Development & Training

Custom ML models tailored to your business problem. We focus on models that work in production, not just in notebooks.

  • Supervised & unsupervised learning
  • Deep learning & neural networks
  • Time series forecasting
  • NLP & text processing
  • Causal inference

MLOps & Deployment Pipelines

Get your models into production with reliable, maintainable infrastructure.

  • CI/CD for ML
  • Model versioning & registry
  • Feature stores
  • Automated retraining
  • A/B testing infrastructure

Evaluation Frameworks

Know when your models are working and when they're not. Production ML requires continuous validation.

  • Offline evaluation pipelines
  • Online experimentation
  • Bias detection
  • Model drift monitoring
  • Business metric alignment

Production Monitoring & Iteration

ML systems need ongoing attention. We build observability that keeps you ahead of problems.

  • Real-time performance dashboards
  • Data quality monitoring
  • Alert systems
  • Model performance tracking
  • Incident response playbooks

Frequently Asked Questions

What's the difference between ML consulting and hiring an ML engineer?

ML consultants bring specialized expertise for specific projects without the overhead of full-time hiring. We're particularly valuable for: (1) projects requiring senior expertise your team lacks, (2) time-sensitive initiatives where ramp-up time matters, (3) architecture decisions that benefit from experience across multiple systems.

How do you price ML consulting projects?

We use deliverable-based pricing with transparent Fair Market Value (FMV) methodology. We break down the project into tasks, map each to a skill tier, and calculate using median market rates. You see exactly how the price is calculated, and bug fixes are always included.

What industries do you work with?

We've built production ML systems across fintech, subscription businesses (causal inference for customer lifecycle at Spotify), biotech (computer vision for Cultivarium), healthcare, and B2B SaaS. Currently we're active in biotech imaging and developing open-source CV tooling. The principles of good ML engineering transfer across domains.

How long does a typical ML consulting engagement last?

Engagements typically range from 4-12 weeks depending on scope. A focused model development project might be 4-6 weeks. A full MLOps infrastructure build with training and handoff is typically 8-12 weeks. We scope precisely so you know what you're getting.

Do you work with existing ML teams or replace them?

We augment, not replace. Most clients have engineering teams but need specialized ML expertise. We work alongside your team, transfer knowledge as we go, and ensure they can maintain the systems after we leave.

Serving Major Tech Hubs

BostonNew YorkSan FranciscoSeattleWashington DC

Ready to Build Production ML?

Tell us about your ML challenges. We'll get back to you within 24 hours with an initial assessment.

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