AI Infrastructure & LLMOps
MLOps
Forecasting, scoring, classification and anomaly detection carry more production load in most enterprises than anything generative. We build the lifecycle around them: deployment, monitoring, retraining and rollback.
The business problem
The model that made the decision is unidentifiable
Models get deployed from a notebook, retrained by hand, and versioned by filename. Six months later nobody can say which version produced a given decision, what data trained it, or whether it still performs. For anything a regulator or an auditor may ask about, that is not a technical debt problem, it is an exposure.
What we do
Make the lifecycle boring
Models are versioned with the data and code that produced them, deployed through a pipeline rather than by hand, and served behind a stable interface. Performance and input distribution are monitored, so drift is detected rather than inferred from business outcomes. Retraining is scheduled or triggered, evaluated against a held-out set, and promoted only if it beats what is live. Rollback is a routine operation.
MLOps
Capabilities
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ML Deployment
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Model Lifecycle Management
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Monitoring
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Retraining
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CI/CD for ML
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Model Registries
Common use cases
Common use cases
- Bring models running from notebooks into a governed deployment pipeline.
- Detect drift on models whose performance nobody currently tracks.
- Establish reproducibility for models supporting regulated decisions.
- Automate retraining that is currently a manual job somebody remembers to run.
How we deliver
How we deliver
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Assess
Establish the constraints: residency, latency, spend and what already runs.
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Architect
Design gateway, routing, caching and failover so provider choice stays reversible.
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Instrument
Observability, evaluation and cost attribution wired in before traffic arrives.
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Operate
Run it against agreed service levels, with capacity and spend reviewed on a cycle.
Technology
Technology
- Kubernetes
- Terraform
- vLLM
- Ollama
- LiteLLM
- OpenTelemetry
- Prometheus
- Grafana
- AWS
- Microsoft Azure
Security & governance
Security & governance
Where data may be processed is a configuration, not an assumption: models can run in your own cloud tenancy or on your own hardware where residency or isolation requires it. Traffic through the gateway is authenticated, attributed to a team and logged, which is what makes both the audit trail and the cost model possible.
Engagement models
Engagement models
AI Project
We take responsibility for designing and delivering a defined AI solution.
Dedicated AI Team
Long-term dedicated engineering capacity built around your stack and delivery model.
Managed AI
We operate, monitor and continuously improve production AI systems.
Why TeamExtension.ai
This is ordinary engineering applied to models
Versioning, pipelines, monitoring and rollback are solved problems in software; they are simply applied inconsistently to models because models arrived through data science rather than engineering. We bring the engineering discipline without discarding what the data science team built.
Selected clients
Frequently asked questions
Frequently asked questions
Do we need a dedicated MLOps platform?
How often should models retrain?
What about reproducibility?
Can this coexist with our generative work?
Related capabilities
Related capabilities
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Managed AI Services
We operate, monitor and continuously improve the AI systems already carrying your production load.
Learn moreDiscuss Your AI Initiative
Lifecycle management for machine learning: deployment, monitoring, retraining and CI/CD.