Act 1
Launch day — the model goes live
The pilot worked. Stakeholders saw the demo. The model is in production — and for a moment, everything looks fine. Accuracy holds. The team moves on to the next project.
Day 1accuracy looks great
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Operate, monitor, and scale enterprise AI reliably — so models stay accurate, governed, and cost-efficient long after launch day.
Keep AI running — monitor, govern, and scale in production.
Dedicated squad on your roadmap, tools, and cadence
Agreed scope, timeline, and price for the outcome
Production ML ops as ongoing team or defined project.
Share your development need — we reply within one business day with scope, timing, and whether a managed team or fixed-cost delivery fits best.
MLOps at enterprise scale
200+ Clients worldwide · 350+ Projects shipped
The production story
Most teams celebrate launch day. Few have the operational infrastructure to keep models performing when data shifts, pipelines fail silently, and costs climb without warning.
“Deployment is tactical. Reliability is strategic.”
The pilot worked. Stakeholders saw the demo. The model is in production — and for a moment, everything looks fine. Accuracy holds. The team moves on to the next project.
Why teams come to us
If any of this sounds familiar, you are ready for structured MLOps — not another pilot.
Sound familiar?
What MLOps delivers
Tell us about your models, stack, and operational challenges — we'll respond within one business day with a practical path forward.
Core capabilities
End-to-end services that keep production models accurate, governed, and high-performing — from classical ML to generative AI.
Reduce deployment time by 50–70%
What this includes
Impact
Reduce deployment time by 50–70%
Accelerators
Pre-built frameworks to jump-start reliable MLOps — without months of setup.
Scored evaluation of deployment, monitoring, and governance with a prioritised roadmap.
Impact
Identify gaps in 2–3 weeks
Pre-built drift, performance, and alerting configs compatible with leading ML frameworks.
Impact
Production monitoring in days, not months
Architecture for LLM serving, prompt management, output monitoring, and cost governance.
Impact
40–55% faster LLM production setup
Playbook for GPU training spend, inference costs, and token usage governance.
Impact
Reduce waste within 2–4 weeks
Technology ecosystem
Hands-on experience across orchestration, serving, monitoring, and cloud ML platforms.
Why Spectrum
We operationalise the models we build — MLOps grounded in real-world deployment, not theory.
200+Happy Clients
Our teams deploy and operate models in production — MLOps is part of delivery, not an afterthought.
Classical ML pipelines, RAG systems, and GenAI agents under one operational framework.
Reliability, compliance, and cost governance where failure is not an option.
Ongoing AI operations squad or scoped MLOps programme — your engagement model.
How to start
Score your deployment, monitoring, and governance practices — leave with a prioritised roadmap.
Production drift and performance monitoring on your stack — without building from scratch.
Full MLOps ownership — CI/CD, serving, monitoring, retraining, and cost governance.
Most teams begin with a maturity assessment or monitoring starter kit, then scale with a managed squad.
Questions
MLOps automates and governs the ML model lifecycle — training pipelines, CI/CD, deployment, and retraining. AI operations extends this to LLM serving, GenAI output monitoring, token cost governance, and responsible AI controls across your full production AI estate.
We implement layered monitoring for prediction performance, data drift, concept drift, and bias — with alerting thresholds and automated retraining triggers configured to your business criticality.
Yes. Our LLMOps capability covers inference infrastructure, prompt management, hallucination detection, token cost attribution, and guardrails — on Azure OpenAI, AWS Bedrock, Vertex AI, or self-hosted deployments.
Production ML pipelines with CI/CD, model serving infrastructure, drift and performance dashboards, automated retraining workflows, cost attribution reporting, and governance documentation — validated against agreed SLAs before handoff.
Yes. We profile GPU and inference workloads, implement right-sizing, quantization, autoscaling, and chargeback dashboards — typically reducing AI infrastructure spend by 30–50%.
With our monitoring starter kit, many teams deploy drift and performance tracking in days rather than months — integrated with your existing ML platform and alerting channels.
Operate with confidence
Move from fragile AI deployments to governed, monitored, and cost-efficient operations. Tell us about your production stack — we will respond within one business day.
Tell us about your models, stack, and operational challenges — we'll respond within one business day with a practical path forward.