Models that keep working after launch
Machine learning operations (MLOps) is the discipline that keeps a model accurate once it meets real data: versioned deployment, monitoring, retraining, and measurement against the outcome the model was bought to move. Winmill builds and runs that layer inside your own Azure environment.
- Enterprise software since 1994
- Microsoft Cloud Solution Provider
- CISSP and CISA certified security leadership
- Fortune 500 clients across North America
Most models decay quietly
A model that tested well last year is quietly wrong today. Data drifts, customer behavior shifts, and the retraining job nobody owns stops running. Most AI initiatives don’t fail at the demo; they fail in month six, when nobody is watching the outputs.
The uncomfortable part: the team that built the model is rarely the team equipped to operate it. Operating models is infrastructure work, and it needs the same rigor as the rest of your production systems.
Operations, not experiments
Winmill builds and runs the operational layer around your models: versioned deployment, monitoring with drift alerts, scheduled retraining, and evaluation against the business number the model exists to move.
All of it lives in your Azure tenancy, in pipelines your own team can read and take over. We built the same discipline into our own 12-week AI platform build, where trained models shipped inside a production delivery pipeline from day one.
The operational layer for your models
Deployment pipelines
Models ship the way software ships: versioned, tested, and promoted through environments with Azure DevOps or GitHub, so any release can be rolled back on purpose instead of rebuilt in a panic.
Monitoring and drift detection
Dashboards and alerts that watch prediction quality and the input data feeding it, so you hear about drift from a system, not from a customer.
Scheduled retraining
Retraining that runs on a calendar or a data trigger, validated against a holdout before promotion, so the model in production is never quietly stale.
Evaluation against outcomes
Every model is measured against the business number it exists to move: forecast error in dollars, hours of manual review removed, conversion lifted. Accuracy scores in a notebook don’t count.
Cost and capacity control
Right-sized compute, batch versus real-time serving decisions, and cost visibility per model, so the system doesn’t erase its own return.
Governance and access
Model registries, approval gates, and audit trails inside your tenancy, aligned to the same access rules the rest of your systems follow.
Run where your data already lives
Your tenancy, your controls
Pipelines, registries, and monitoring are built inside your own Azure environment, under your identity and access rules. Nothing about your models leaves your control.
Security is in-house
The practice that penetration tests and audits code for a living is a Winmill practice, not a subcontractor. The pipelines that move your models get the same review as the systems we build.
Real data stays in production
Development and testing run against fictitious data of identical structure. Your real data lives only in your production environment, where it belongs.
Three ways in
Models in production, nobody owns them
We take over operation of models your team or a vendor built: instrument them, put them in pipelines, and give you the first honest read on whether they still work.
Models stuck in notebooks
A model that works on a laptop is halfway. We build the first deployment pipeline, monitoring, and rollback path that turns it into a system.
No models yet
If the open question is what AI should do with your data in the first place, start with the Data & Intelligence practice and the two minute Fabric fit check.
Frequently asked questions
What is MLOps?
Machine learning operations: the practices that keep a model reliable in production. Software has DevOps; models need the same thing, plus monitoring for the ways models uniquely fail, like data drift and silent accuracy decay. If a model matters to your business, someone has to run it. MLOps is that job, done as engineering instead of heroics.
Can you take over models we didn’t build?
Yes. Takeover engagements start with an inventory: what models exist, what decisions they feed, what they run on, and whether anyone would notice if one went wrong. From there we instrument first, then move them into versioned pipelines. You get an honest assessment before you commit to anything larger.
What tools do you use?
Your stack first. On Azure that typically means Azure Machine Learning or Microsoft Fabric for the data and the models, Azure DevOps or GitHub for pipelines, and standard monitoring you already operate. We don’t bring a proprietary platform you’d be stuck with.
What does an engagement cost?
MLOps work starts as a fixed price engagement with a not-to-exceed cap and dates set at kickoff, scoped to the models you have. Ongoing operation, if you want it, is priced as a defined monthly scope, and everything we build stays in your tenancy either way.
Find out what your models are doing right now
Book a 30-minute walkthrough with Eddie Hudson and leave with an honest read on the models you are running and a concrete next step.