How do you handle model monitoring and drift?

Feature drift (Population Stability Index), prediction drift (distribution shift), and outcome tracking (once ground truth arrives): all tracked automatically. Drift beyond thresholds triggers PagerDuty and opens a retrain PR. You find out from the alert, not from the business dashboard six weeks later.

Can you build model risk documentation for our SR 11-7 process?

Yes. Every enterprise ML engagement includes: development validation report, ongoing monitoring plan, challenger model comparison, adverse action reason code mapping, and model card. Your model risk management team gets a docs pack aligned to SR 11-7 / OCC 2011-12.

How do you handle feature store rollout without breaking existing pipelines?

Incrementally. New features land in the feature store first; existing features migrate one team at a time, with dual write for a validation period. No 'big bang' migration. Typical feature store rollout: 3 to 6 months for a 20 model estate.

What's a realistic first engagement scope?

Three common starting points: (1) MLOps foundation build (6 to 10 weeks) for teams shipping ad hoc, (2) production ML for one high value use case (8 to 12 weeks), or (3) feature store deployment + first model migrations (10 to 14 weeks). We recommend based on your team's current maturity.

AI & Machine Learning

Enterprise Machine Learning Development

Production ML pipelines your risk team signs off: feature stores, drift monitoring, explainability, and audit trails on every prediction.

Request Confidential Briefing Talk to an architect

What is enterprise machine learning engineering and when do you need it?

The Answer

Enterprise ML engineering takes models from notebook to production (pipelines, feature stores, monitoring, retraining, and governance that satisfy audit requirements) so a model that scored well in a lab keeps scoring well against real users, real drift, and real regulators. You need it when data scientists produce models that never ship, or shipped models silently degrade until someone notices in a QBR. The outcome is ML that runs reliably and is measured continuously, with a monthly report your risk team can defend.

What you get

Three outcomes we commit to before we start.

MLOps that survives audit

Every model has a lineage: which data trained it, which code shipped it, which config runs it in production. Reproducing any prediction from six months ago is one command, not a two week archaeology dig.

Feature store as the single source of truth

Feast or Tecton feature stores prevent the classic 'training serving skew' bug. The same feature definitions that trained your model are the ones that score in production: with online/offline consistency guarantees.

Model risk documentation on delivery

For regulated deployments we ship SR 11-7 / OCC 2011-12 aligned model risk docs: development validation, monitoring plan, challenger model, and adverse action reason mapping. Your model risk team gets what they need without a scramble.

The Guaranteed Production Pilot

Fixed scope · Written target

A production Enterprise ML system in your VPC: audited, documented, owned by your team.

Not a slide deck and not a sandbox demo: a working Enterprise ML deployment inside your own cloud boundary, mapped to your compliance controls and handed over with the schema, the eval harness, and the runbook.

Speed

Architecture and success criteria signed off in week one. First working slice running in your environment inside 30 days.

Zero effort

Fully done for you. Our senior squad owns ontology, build, evals, and compliance mapping: your team reviews and signs off, nothing more.

Risk reversal

Fixed scope, fixed price, and a measurable success target agreed in writing before we start. Miss the target and you don't pay for the pilot.

Request a scoped pilot

Related services

More in AI & Machine Learning.

Service

Enterprise Data Warehouse Modernization

Move off Teradata, Netezza, and on-prem Oracle onto a cloud lakehouse your AI can actually query. Governed, streaming, and vector-ready: migrated in phases, in your VPC.

Explore

Service

AI Agent Development

Production AI agents for regulated industries, grounded in enterprise knowledge graphs, GraphRAG, and MCP servers, so answers are traceable and the audit trail exists. Delivered in fixed-price 6-week pilots by senior engineers with 12+ years of experience.

Explore

Service

Custom AI Agent Development

AI agents engineered for your domain (your data model, your workflows, your compliance regime) not a generic platform you bend to fit. Grounded in enterprise knowledge graphs, GraphRAG, and MCP servers, delivered in fixed-price 6-week production pilots by senior engineers with 12+ years each.

Explore

Industries we serve with this