Service · AI · MLOps
The infrastructure that keeps your models alive in production.
The operational layer under your models — deployment, monitoring, drift detection, retraining, and governance — built in your own cloud, owned by you, steady state in 4–8 weeks.
A model is a system, not an event
The real problem
Why trained models stop working months after launch.
A model is a prediction about a world that keeps changing — the day it launches is as accurate as it will ever be. Then behavior shifts, a data feed breaks, and it keeps returning confident answers that are quietly wrong until a number moves the wrong way.
That gap is what MLOps closes. The deployment pipelines, monitoring, drift detection, retraining, and governance around a model are what turn one that works once into a system that keeps working.
Of machine-learning models degrade over time — peer-reviewed across 32 datasets and four industries.
Vela et al., Scientific Reports, 2022 ↗
Of generative-AI projects are abandoned after proof of concept because organizations can't operationalize them.
Gartner, July 2024 ↗
Where it does the work
Where MLOps does the work — and what each capability delivers.
Not one tool — a set of operational capabilities, each closing a specific way models fail in production.
Model deployment pipelines (CI/CD for ML)
Promotes a trained model to production through automated build, test, and approval gates — the way you ship code.
A fragile manual handoff becomes a repeatable, reversible deploy.
Production monitoring & observability
Tracks live prediction quality, latency, data health, and serving cost — with alerts when any breaches its threshold.
A silently-failing model becomes a paged incident — caught in hours, not quarters.
Drift detection
Watches for the moment incoming data diverges from what the model was trained on — the leading indicator of model aging.
Degradation is caught at the cause, not after the damage.
Automated retraining
A drift threshold, a schedule, or a performance floor triggers retraining on fresh data, revalidated against a baseline — promoted only if it wins.
Models stay current without a standing manual project.
Model governance & audit
Versions every model, dataset, and metric, records who approved what, and keeps the lineage and explainability trail a regulator can inspect.
Model risk becomes auditable instead of a black box.
AI infrastructure management
Provisions and manages the compute, GPUs, model registry, and feature store the lifecycle runs on, and keeps serving cost under control.
Capacity and cost stop being a surprise.
As of June 2026 · revisit quarterly
What the missing operational layer costs — the measured impact.
Independent industry findings — cited as third-party evidence, not Silicon Prime's own client results.
Of ML models degrade over time unless monitored and maintained — peer-reviewed across 32 datasets, four industries.
Vela et al., Scientific Reports, 2022 ↗
Of generative-AI projects abandoned after proof of concept by end of 2025 — for want of an operational path.
Gartner, 29 Jul 2024 ↗
Of agentic-AI projects canceled by end of 2027 — poll of 3,400+ orgs — on escalating cost and inadequate controls.
Gartner, 25 Jun 2025 ↗
What's included
What our MLOps services cover.
The operational layer under your models — distinct from building the model itself.
MLOps assessment & target architecture
We audit how your models reach production and where they break, then design the pipeline, registry, monitoring, and governance to fit your cloud — the honest "you don't need a full platform" call included.
Deployment pipelines & model registry
CI/CD for models — packaging, testing, versioning, staged promotion, rollback — on a registry that's the single source of truth for what's running where.
Monitoring, observability & alerting
We instrument live prediction quality, data health, latency, and serving cost — dashboards and alerts that page someone when a threshold breaks.
Drift detection & automated retraining
We set the thresholds, wire the retraining pipeline, and gate every retrained model against a baseline — only a better model ships, no manual project.
Model governance & compliance
We version models, datasets, and metrics, capture approvals and lineage, and build the audit trail regulated functions require — with human-in-the-loop gates where it matters.
Infrastructure, cost control & enablement
We provision and manage the compute, GPUs, and feature store the lifecycle runs on, keep serving cost visible, and train your team to own it.
What you get when you hire us — all assigned to you
How it runs
How an MLOps engagement runs.
The same delivery model behind all our AI development work — one accountable lead, fixed scope, no handoffs.
STEP 01
Assess
Map how models reach production today, where they fail, and the gaps in monitoring and governance.
Output: a target architecture & the reliability metrics
STEP 02
Pipeline
Build the deployment pipeline, registry, and infrastructure in your own cloud, and get a first model flowing through it end to end.
Output: a CI/CD path from trained model to served prediction, with rollback
STEP 03
Observe
Instrument monitoring, drift detection, and cost tracking, and stand up the alerting and dashboards.
Output: live observability & a retraining loop gated on a baseline
STEP 04
Operate & enable
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
Track record
The production discipline an MLOps layer is made of.
MLOps is the discipline of keeping software dependable in production after it ships — the thing we're known for, proven on a system that has run reliably for years.
A Stanford-rooted Responsible AI lab, founded 2011, run by founder Kelvin Tran — 20+ years of production engineering. We'll tell you plainly when a full platform is more than your problem needs.
Aegis AI · 200+ locations · 4+ years
For BJ's Restaurants, our Aegis AI process moved release cadence from every two weeks to twice a week with zero critical defects sustained over four years — on pre-release quality gates, staged rollout, and continuous monitoring. That's the same loop an MLOps platform runs: automated promotion, a gate that blocks a bad release, monitoring that catches a problem before it spreads — applied to models instead of code.
Why build your MLOps platform with us.
Operations is the whole job, not a sub-bullet. Deployment, monitoring, drift, and governance are the product — the layer most AI projects are missing when they stall.
Responsible AI is the founding charter. Governance, audit trails, and human-in-the-loop gates are 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.
Founder-led, one accountable lead. 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.
Where it earns its keep first
Where a disciplined MLOps layer earns its keep first.
Fintech
Fraud, credit, and real-time-decisioning models where drift detection and a full audit trail aren't optional.
Fintech software →Healthcare
Clinical and operational models inside HIPAA-compliant architectures, where every prediction must be logged and governed.
Healthcare software →Ecommerce & retail
Forecasting, recommendation, and pricing models that retrain on fresh behavior — monitored so a seasonal shift never silently breaks them.
Ecommerce software →Manufacturing & operations
Predictive-maintenance and quality-vision models where uptime depends on the model staying accurate as conditions change.
Manufacturing software →Questions buyers ask before they hire.
Thirty minutes · no pitch deck
Ready to stop your models from quietly failing in production?
Bring the models you can't keep reliable — we'll tell you honestly what operational layer they need, what it takes to build, and what it costs to run.