MLOps Engineering
Production ML infrastructure: model serving, feature stores, experiment tracking, and CI/CD for machine learning. We build MLOps platforms that move models from notebook to production reliably.
What happens after you submit specs
1. Context
We inspect the system, constraints, and where delivery or architecture risk is most likely to surface.
2. Recommendation
You get a direct recommendation: audit, advisory track, scoped build, or a clear signal that the work is not ready yet.
3. Next Step
If there is a fit, we define the shortest path to a useful engagement and a production-ready outcome.
ML Systems Beyond the Notebook
We engineer MLOps infrastructure that moves models from notebook to production with experiment tracking, automated deployment, feature consistency, and model observability — so the data science team can iterate without manual handoffs.
Typical engagement starts when
- model deployment is a manual process with no rollback, no versioning, and no confidence in what is actually serving traffic
- training and serving feature pipelines have diverged, causing silent quality degradation in production
- the team is drowning in experiment tracking spreadsheets or has no record of which hyperparameters produced which results
- ML CI/CD is missing: model changes go to production without automated testing, evaluation, or approval workflows
What We Build
| Capability | What We Deliver |
|---|---|
| Model serving | Ray Serve, BentoML, or custom serving infrastructure with autoscaling, health checks, and canary deployment |
| Feature stores | Feast or custom feature pipelines ensuring training/serving consistency with point-in-time correctness |
| Experiment tracking | MLflow or Weights & Biases integration with hyperparameter logging, artifact storage, and model registry |
| ML CI/CD | Automated testing, evaluation gates, and deployment pipelines triggered by model registry events |
Engineering Standards
- Model versioning with immutable artifacts: every production deployment traceable to exact training run, data snapshot, and hyperparameters
- Feature store with point-in-time correctness: prevent data leakage between training and serving
- A/B deployment with automatic rollback: canary traffic routing with quality thresholds that trigger rollback without human intervention
- Drift detection with alerting: statistical monitoring of feature distributions and model outputs against baseline behavior
- Resource right-sizing: GPU/CPU allocation matched to actual inference requirements, not worst-case provisioning
When to Use This
| If Your Situation Is | Then We Recommend |
|---|---|
| Model deployment is manual with no versioning or rollback capability | MLflow model registry + automated deployment pipeline |
| Feature engineering done differently in training vs. serving | Feast feature store with consistent transformation logic |
| GPU serving costs growing without visibility into utilization | Ray Serve with autoscaling and resource monitoring |
| No automated testing or evaluation gates for model changes | ML CI/CD with evaluation benchmarks and approval workflows |
| Experiment tracking is spreadsheets or missing entirely | MLflow or Weights & Biases with hyperparameter logging and artifact storage |
| ML system is early-stage and infrastructure is premature | Start with manual deployment; plan MLOps when iteration cycle justifies investment |
MLOps Maturity Spectrum
| Level | Characteristics | When to Invest |
|---|---|---|
| Level 0 | Manual deployment, no versioning, experiments in notebooks | Model in production, any deployment |
| Level 1 | Model registry, basic CI/CD, experiment tracking | Multiple models or frequent retraining |
| Level 2 | Feature store, automated retraining, drift detection | Training/serving skew issues, data freshness requirements |
| Level 3 | Full platform, multi-tenant, self-service | Multiple teams, dozens of models, platform as product |
Most organizations benefit from Level 1-2. Level 3 is only justified when ML is a core platform capability with multiple consuming teams.
Common failure patterns we fix
- model serving deployed without health checks, causing silent failures when inference crashes
- feature pipelines reimplemented for serving, introducing training/serving skew that degrades quality
- experiment tracking started after months of work, losing the lineage needed to reproduce best results
- GPU provisioning sized for peak load, wasting cost during normal traffic
- model rollback requiring manual intervention instead of automated quality threshold triggers
What you leave with
- model serving infrastructure with health checks, autoscaling, and canary deployment
- experiment tracking with hyperparameter logging and model registry integration
- feature pipelines with training/serving consistency and point-in-time correctness
- CI/CD pipelines that automate testing, evaluation, and deployment approval
- operational runbooks for deployment, rollback, and drift response
Best Fit
- Team has models in production with manual deployment and no versioning
- Organization experiences training/serving skew or feature inconsistency
- Data science team spends time on deployment mechanics instead of modeling
- Multiple models or frequent retraining cycles justify automation
Depth of Practice
We build MLOps infrastructure for anomaly detection pipelines, recommendation systems, and foundation model serving. Production deployments include MLflow-tracked experiments, Feast feature stores, and Ray Serve clusters handling thousands of inference requests per second with sub-100ms latency.
Related articles
Model Selection for Business Problems: Classification, Regression, Ranking, and the Questions That Determine Architecture
How to match business problems to model families — classification, regression, ranking, or generation — before touching a hyperparameter.
MLOpsFeature Engineering That Survives Production: Drift Detection and the Features That Break
80% of production ML failures trace to features, not models. Here's which feature types break first and how to detect and prevent drift before it reaches users.
AI EngineeringWhen Classical ML Beats LLMs: The Decision Framework for Model Selection in Production
A decision framework for choosing between classical ML and LLMs in production — covering cost, latency, interpretability, and the hybrid architecture that combines both.
Discuss your MLOps Engineering path
Submit system context, constraints, and delivery pressure. A Principal Engineer reviews every submission and recommends the right next step.
1. Context
We review the system, constraints, and where risk is most likely to surface.
2. Recommendation
You get a direct recommendation: audit, advisory, sprint, or pause.
3. Next Step
If there is a fit, we define the shortest useful engagement.
No SDRs. A Principal Engineer reviews every submission.