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
MLOps & AI Operations
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.
- Managed team
Dedicated squad on your roadmap, tools, and cadence
- Fixed-cost delivery
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
What happens after the demo ends
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.”
Why teams come to us
AI in production requires more than a deployed model
If any of this sounds familiar, you are ready for structured MLOps — not another pilot.
Sound familiar?
- Models drift silently — nobody notices until accuracy drops or customers complain
- LLM token costs grow month over month with no attribution by team or application
- Deployments are manual, slow, and risky — rollbacks take days not minutes
- Compliance and security teams cannot audit model decisions or deployment changes
- Data science and engineering operate in silos — no shared pipeline or registry
What MLOps delivers
- Automated deployment and lifecycle management across ML and LLM environments
- Continuous monitoring for performance, drift, and bias in production
- Cost governance with attribution, right-sizing, and spend controls
- Security, reliability, and compliance across your production AI estate
Let's talk about your production AI
Tell us about your models, stack, and operational challenges — we'll respond within one business day with a practical path forward.
- ISO 27001:2022 certified
- ML + LLM operations
- Same-day response
Core capabilities
Integrated MLOps & AI operations
End-to-end services that keep production models accurate, governed, and high-performing — from classical ML to generative AI.
ML pipeline automation & CI/CD
Reduce deployment time by 50–70%
What this includes
- End-to-end pipeline design and orchestration
- Automated training, validation, and versioning
- Deployment gating based on evaluation thresholds
- Rollback mechanisms and feature pipeline automation
Impact
Reduce deployment time by 50–70%
Accelerators
Faster path to production stability
Pre-built frameworks to jump-start reliable MLOps — without months of setup.
MLOps maturity assessment
Scored evaluation of deployment, monitoring, and governance with a prioritised roadmap.
Impact
Identify gaps in 2–3 weeks
Model monitoring starter kit
Pre-built drift, performance, and alerting configs compatible with leading ML frameworks.
Impact
Production monitoring in days, not months
LLMOps deployment blueprint
Architecture for LLM serving, prompt management, output monitoring, and cost governance.
Impact
40–55% faster LLM production setup
AI cost governance framework
Playbook for GPU training spend, inference costs, and token usage governance.
Impact
Reduce waste within 2–4 weeks
Technology ecosystem
Platforms we operationalise in production
Hands-on experience across orchestration, serving, monitoring, and cloud ML platforms.
ML orchestration
- Kubeflow
- MLflow
- ZenML
- Metaflow
- Apache Airflow
Model serving
- NVIDIA Triton
- vLLM
- TorchServe
- TensorFlow Serving
- Seldon
- BentoML
Monitoring & observability
- Evidently AI
- WhyLabs
- Arize
- Fiddler
- Grafana
- LangSmith
Cloud ML platforms
- Azure ML
- AWS SageMaker
- Google Vertex AI
- Databricks MLflow
- Azure OpenAI
- AWS Bedrock
Why Spectrum
Operations expertise built on delivery experience
We operationalise the models we build — MLOps grounded in real-world deployment, not theory.
200+Happy Clients
We run what we build
Our teams deploy and operate models in production — MLOps is part of delivery, not an afterthought.
ML to LLMs — full spectrum
Classical ML pipelines, RAG systems, and GenAI agents under one operational framework.
Built for enterprise complexity
Reliability, compliance, and cost governance where failure is not an option.
Managed team or fixed-cost
Ongoing AI operations squad or scoped MLOps programme — your engagement model.
How to start
Move from fragile deployments to governed operations
- 2–3 weeks · Structured review
MLOps maturity assessment
Score your deployment, monitoring, and governance practices — leave with a prioritised roadmap.
- Maturity scorecard
- Gap analysis
- 90-day improvement plan
- Days · Starter kit
Deploy monitoring fast
Production drift and performance monitoring on your stack — without building from scratch.
- Monitoring configs
- Alerting setup
- Dashboard handover
- Ongoing · Dedicated squad
Managed AI operations
Full MLOps ownership — CI/CD, serving, monitoring, retraining, and cost governance.
- Production pipelines
- SLA-backed ops
- Governance documentation
Most teams begin with a maturity assessment or monitoring starter kit, then scale with a managed squad.
Questions
Frequently asked questions
What is the difference between MLOps and AI operations?
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.
How do you approach model monitoring in production?
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.
Do you support LLM and GenAI operations?
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.
What does an MLOps engagement deliver?
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.
Can you reduce our AI infrastructure costs?
Yes. We profile GPU and inference workloads, implement right-sizing, quantization, autoscaling, and chargeback dashboards — typically reducing AI infrastructure spend by 30–50%.
How quickly can we get production monitoring in place?
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
Keep your AI running — not just deployed
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.
Let's talk about your production AI
Tell us about your models, stack, and operational challenges — we'll respond within one business day with a practical path forward.
- ISO 27001:2022 certified
- ML + LLM operations
- Same-day response