MLflowKubeflowFeastRay ServeBentoMLWeights & Biases

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.

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What you get back

1. Diagnosis What works, what is blocked, and why.
2. Recommendation Audit, advisory, sprint, or pause.
3. Scope Next action, boundaries, and timing.

// Model deployment status

$ mlflow models serve --model anomaly-detector-v3

✓ Serving on port 5001 · GPU: A100

✓ Accuracy: 99.2% · F1: 0.97

✓ Monitoring: Prometheus + Grafana

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.

Evidence

Deployments in this area

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Kafka Isolation Forest

Real-time anomaly detection processing 2.4M events/day with 70% fewer false positives

How we built a real-time anomaly detection pipeline processing 2.4M events/day using Kafka, Isolation Forest, and foundation models. False positive rate reduced from 68% to under 20%.

events_day: 2.4M

Read case study →

Engineering Intelligence

AI Strategy

Discuss your MLOps Engineering path

Send the system context, constraints, and pressure. A Principal Engineer reviews it and recommends the next step.