MLOps and LLMOps

Overview

A model that worked on launch day can drift, slow down or cost more each month. We set up the pipelines, checks and alerts that catch it.

We can operate the system for you after go-live, with a monthly report on quality and cost, or set it up and train your team to run it.

Who it is for

• Teams with models in production and no clear view of quality or cost.
• Companies moving several AI pilots into production at once.
• Leaders who need audit trails for models that affect customers.

What you get

• Model deployment pipelines with rollback.
• Evaluation that runs on every model, prompt or data change.
• Monitoring for quality, drift, latency and cost.
• Spend budgets and alerts per product and per team.
• A model registry and a record of what changed and why.
• Runbooks and an on-call rota for the models you depend on.

How it runs

1. Review the models in use, how they are deployed and what is measured.
2. Set up evaluation, monitoring and cost tracking.
3. Automate deployment with checks and rollback.
4. Hand over with runbooks, or move into a managed run.

In practice

• Inference costs cut by routing simple requests to a smaller model.
• A prompt change blocked because the evaluation set caught a regression.
• A fraud or credit-risk model retrained automatically when drift crosses a threshold.

Technology

• MLflow, Weights & Biases and cloud model registries.
• Langfuse, LangSmith and OpenTelemetry for tracing.
• Kubernetes, serverless and managed inference endpoints.
• Grafana and cloud dashboards for quality and cost.

How we charge

A fixed-scope setup, then an optional monthly managed run.

Questions