The infrastructure that keeps your models alive in production.
We build and run the operational layer underneath your AI: deployment pipelines that ship a model the way you ship code, monitoring that catches drift before your customers do, automated retraining, and the governance trail your risk team needs.
For the model you already trained, or the dozen you can’t keep reliable at scale — built inside your own cloud, owned entirely by you. Fixed scope, one accountable lead, steady state in 4–8 weeks.
Because a model is not software you ship once — it is a prediction about a world that keeps changing. The day it launches it is as accurate as it will ever be.
Then customer behavior shifts, an upstream data feed changes format, a feature pipeline silently breaks, and the model keeps returning confident answers that are quietly wrong. Nobody notices until a number moves the wrong way.
That is the gap MLOps closes — and it’s the same gap that strands AI projects short of value entirely. A model in a notebook returns nothing. MLOps — the deployment pipelines, monitoring, drift detection, retraining, and governance around it — is what turns a model that works once into a system that keeps working.
MLOps isn’t one tool. It’s a set of operational capabilities, each closing a specific way models fail in production. For each: what it does, the benefit it produces, and a one-line illustration of the help.
Packages a trained model, its dependencies, and its feature logic, and promotes it to production through automated build, test, and approval gates — the way you already ship code. Benefit — releases go from a fragile manual handoff to a repeatable, reversible deploy, so a new version is minutes of pipeline instead of weeks of coordination, and a bad version rolls back instead of staying live.
For example, a data-science team that used to email a pickle file to engineering and wait two weeks pushes a model behind a tested pipeline and sees it serving traffic the same afternoon — with a one-click rollback if the metrics dip.
Tracks live prediction quality, latency, input-data health, and serving cost on a dashboard, with alerts when any of them break their thresholds. Benefit — a silently-failing model becomes a paged incident, so problems are caught in hours instead of quarters.
For example, an upstream feed starts sending nulls in a key feature at 3 a.m.; the monitor fires before the morning batch runs, instead of the error surfacing weeks later in a revenue report nobody could explain.
Watches for the moment incoming data or the prediction distribution diverges from what the model was trained on — the leading indicator of the “AI aging” that degrades 91% of models. Benefit — degradation is caught at the cause, not after the damage, because you learn the world shifted before accuracy visibly tanks.
For example, a fraud model trained pre-holiday starts seeing a buying pattern it has never met; drift detection flags the shift and triggers a review before the false-positive rate spikes and blocks real customers.
Defines the triggers — a drift threshold, a schedule, a performance floor — that kick off retraining on fresh data, revalidate against a baseline, and promote the new model only if it wins. Benefit — models stay current without a standing manual project, so accuracy is maintained continuously instead of decaying until someone notices and scrambles.
For example, a demand-forecasting model retrains automatically each week on the latest sales, holds its accuracy through a seasonal shift, and never ships a worse version because the baseline gate blocks it.
Versions every model, dataset, and metric, records who approved what, and keeps the lineage and explainability trail a regulator or risk officer can inspect. Benefit — model risk becomes auditable instead of a black box, which is the difference between deploying in a regulated function and not being allowed to.
For example, when a regulator asks why a credit model declined an applicant, the team produces the exact model version, training data, and approval record in minutes instead of reconstructing it from memory.
Provisions and manages the compute, GPUs, model registry, and feature store the lifecycle runs on — and keeps serving cost visible and under control. Benefit — capacity and cost stop being a surprise, so models scale on reliable infrastructure without idle GPUs quietly burning the budget.
For example, a serving cluster auto-scales for a traffic spike and scales back down after, instead of running an over-provisioned, always-on bill that nobody had time to right-size.
This is the operational layer under your models — distinct from building the model itself. The scope below is what separates a model that survives in production from one that quietly rots.
We audit how your models get to production today, where they break, and what’s missing — then design the pipeline, registry, monitoring, and governance to fit your cloud and your team, run as an AI readiness assessment scoped to operations. The honest “you don’t need a full platform for this” call is included.
We build CI/CD for models — automated packaging, testing, versioning, staged promotion, and rollback — backed by a registry that is the single source of truth for what’s running where.
We instrument live prediction quality, data health, latency, and serving cost, with dashboards your team reads and alerts that page someone when a threshold breaks — so a degrading model is an incident, not a surprise.
We set the drift and performance thresholds, wire the retraining pipeline, and gate every retrained model against a baseline so only a better model ships. The loop runs without a standing manual project.
We version models, datasets, and metrics, capture approvals and lineage, and build the explainability and audit trail regulated functions require — with human-in-the-loop review gates where a prediction carries real consequence.
We provision and manage the compute, GPUs, and feature store the lifecycle runs on, keep serving cost visible, and train your team to operate the platform — read the dashboards, approve a deploy, trigger a retrain — and own it when we step back.
What you get when you hire us — all assigned to you
The same delivery model behind all our AI development work, tuned for operations — one accountable lead, fixed scope, no handoffs.
Map how models reach production today, where they fail, and the gaps in monitoring and governance.
Output: a target architecture & the reliability metrics we’ll be judged on
Build the deployment pipeline, registry, and infrastructure in your own cloud tenant, and get a first model flowing through it end to end.
Output: a working CI/CD path from trained model to served prediction, with rollback
Instrument monitoring, drift detection, and cost tracking, and stand up the alerting and dashboards.
Output: live observability & a retraining loop gated on a baseline
Run it in shadow, then production, with governance gates in place, and train your team to own the platform.
Output: a production MLOps platform & a team that operates it
Steady state in 4–8 weeks, full IP assignment signed at kickoff, payment tied to the reliability we agree to deliver — not hours billed.
MLOps is, at its core, the discipline of keeping software dependable in production after it ships — pipelines, staged rollout, and relentless monitoring. That discipline is the thing we are known for, proven on a system that has run reliably for years.
For BJ’s Restaurants — a 200+ location chain whose operations are software-critical — we applied our Aegis AI delivery process to exactly this problem: not building a model, but engineering how work reaches and stays in production.
Over 4+ years, we moved their release cadence from every two weeks to twice a week while sustaining zero critical defects, on the back of pre-release quality gates, staged rollout, and continuous production monitoring (BJ’s Restaurants). A traditional enterprise now ships at the cadence and stability of a frontier tech company.
That is the same loop an MLOps platform runs — automated promotion, a quality gate that blocks a bad release, and monitoring that catches a problem in production before it spreads — applied to models instead of application code.
Silicon Prime is a Stanford-rooted Responsible AI lab, founded in 2011, run by founder Kelvin Tran — 20+ years of production engineering, personally accountable for every engagement. We don’t claim a published MLOps-platform case study for every industry above; what we can show is a multi-year record of keeping software dependable in production, and the honesty to tell you when a full platform is more than your problem needs — which a vendor selling one by the seat won’t.
What sets our MLOps services apart is a track record of operating software reliably in production for years, not a platform demo:
Operations is the whole job, not a sub-bullet. We treat deployment, monitoring, drift, and governance as the product — the exact layer that decides whether a model returns anything, and the one most AI projects are missing when they stall.
Responsible AI is the founding charter. Governance, audit trails, and human-in-the-loop gates aren’t an add-on for us — they’re how a model earns the right to run in a regulated or high-stakes function.
Cloud- and tool-neutral. We build on your cloud and the stack that fits your team, not a platform we resell. No license commitment steers the architecture.
Founder-led, one accountable lead. No account managers, no handoffs — the person who scopes it answers for it.
Built to transfer. Pipelines, dashboards, infrastructure-as-code, and runbooks are assigned to you, and your team is trained to run the platform when we step back. You own the capability, not a dependency.
Fraud, credit, and real-time-decisioning models where drift detection and a full audit trail aren’t optional, and a wrong prediction is a regulatory and financial event. Fintech software →
Clinical and operational models inside HIPAA-compliant architectures, where every prediction must be logged, explainable, and governed before it runs. Healthcare software →
Forecasting, recommendation, and pricing models that retrain on fresh behavior weekly, monitored so a seasonal shift never silently breaks them.
Predictive-maintenance and quality-vision models on the line and across the fleet, where uptime depends on the model staying accurate as conditions change.
What teams want to know before they put an MLOps layer under their models.
Machine learning development is about building the model — framing the problem, engineering features, training, and validating it against a baseline. MLOps is the operational layer that keeps that model — or any model your team already built — alive in production: deployment pipelines, monitoring, drift detection, retraining, and governance. One ends with a trained model; the other begins there. Many engagements need both, and we’ll scope which one your problem actually calls for — sometimes you have good models and just can’t keep them reliable, which is squarely an MLOps job.
Yes — that’s a common starting point. We assess how your existing models are deployed and monitored, find where they’re silently degrading or ungoverned, and build the pipeline, observability, drift detection, and retraining around them. You don’t need to rebuild the models to get them onto a reliable operational footing.
Monitoring plus drift detection. We instrument live prediction quality, input-data health, and serving cost with alerting on every threshold, and we watch for drift — the divergence between incoming data and what the model was trained on — which is the leading indicator of the “AI aging” that degrades 91% of models over time. When drift or decay trips a threshold, retraining is triggered and the new model is gated against a baseline before it ever ships.
Every retrained model is validated against the current production baseline on a held-out, production-realistic split before it’s promoted — and if it doesn’t win on the metric that matters, the pipeline blocks it and keeps the existing model live. Retraining is automatic; promotion is earned. That baseline gate is the difference between a retraining loop that maintains accuracy and one that quietly degrades it.
No. We build on your cloud and choose tooling that fits your team and stack rather than a platform we resell — open-source or managed, whichever serves you best. The pipelines, infrastructure-as-code, and runbooks are yours, so there’s no vendor steering the architecture and no license you’re trapped under.
The platform runs inside your own cloud tenant under your access controls, and every engagement starts with an NDA and a security review. We version every model, dataset, and metric; record approvals and lineage; and build the explainability and audit trail regulated functions require — with human-in-the-loop review gates where a prediction carries real consequence. That auditability is what makes deploying in fintech and healthcare defensible rather than risky.
You do — completely. The deployment pipelines, monitoring and governance setup, infrastructure-as-code, and runbooks transfer under full work-for-hire IP assignment signed at kickoff, and your team is trained to operate and extend the platform. The engagement is built around the handover — keep us on a reduced retainer or take the keys.
Most engagements reach steady state in 4–8 weeks under a fixed-scope arrangement with one accountable lead, and payment is tied to the reliability outcomes we agreed to deliver. Build cost depends on scope and how many models you’re operationalizing — our AI development cost guide gives real ranges — and we model the ongoing infrastructure and serving cost before building, so the running bill is a forecast you’ve already seen.
Thirty minutes · No pitch deck
Bring the models you can’t keep reliable — or the ones stuck short of production — and we’ll tell you honestly what operational layer they need, what it takes to build, and what it costs to run.