SERVICE

MLOps and LLMOps

Models that stay correct after the people who built them move on.

What this actually is

MLOps is what turns a model into a system somebody can operate. Versioning, reproducible training, deployment with rollback, and monitoring that detects quality falling before a customer reports it.

LLMOps is the same discipline against a harder problem, because the output is not comparable to a label. Quality is judged by evaluation sets, graders and human review rather than by an accuracy number, and cost per request becomes a first-class metric in a way it never was for a classifier.

What we do

Reproducibility
Training that can be re-run and produce the same model — data version, code version, seed and environment captured together.
Evaluation
Held-out sets and graders running on every change, so a prompt tweak or a model upgrade cannot silently degrade quality.
Deployment
Versioned serving with canary releases and a rollback that does not require a rebuild.
Monitoring
Input drift, output distribution, latency and cost — with alerts on the ones that indicate a real problem rather than on all of them.
Cost control
Per-request and per-tenant cost tracking, because inference spend grows quietly and is invisible in a monthly total.

The stack

MLflowWeights & BiasesLangSmithAirflowKubernetesPrometheus and GrafanaDVCDocker

What it connects to

Integration surface is the honest driver of effort — ten systems is not ten times one system.

Model registries and artifact stores
Data warehouses and feature stores
CI providers for evaluation on pull requests
Model providers — OpenAI, Anthropic, self-hosted
Observability and alerting platforms

How a project runs

Audit

1–2 weeks

What is deployed, whether it can be reproduced, and how anyone would know if it broke.

Foundations

3–5 weeks

Versioning, evaluation harness and a deployment path with rollback.

Monitoring

2–3 weeks

Drift, quality and cost tracked with alerts tied to a response.

Handover

1–2 weeks

Your team running it, with the runbooks written.

Where teams use it

Post-prototype

Models that work in notebooks and have no path to production — the most common engagement here.

Model sprawl

Several teams deploying independently with no shared standard or registry.

Regulated sectors

Where reproducing a decision from six months ago is a requirement rather than a nicety.

Cost surprises

An LLM bill growing faster than usage, usually because nothing is cached or metered.

Silent degradation

Quality that fell gradually and was noticed by a customer rather than a dashboard.

When this is the wrong answer

With one model and one engineer, most of this is overhead. It earns its place at several models, several people, or a regulatory requirement.

Monitoring without a response plan is a dashboard. Every alert needs a defined action, or it becomes noise and then gets muted.

Evaluation sets go stale. A harness that has not been updated in a year is measuring last year's problem and giving false confidence.

You cannot retrofit reproducibility onto a model whose training data no longer exists. That one has to be designed in.

Frequently asked questions

How is LLMOps different from MLOps?

The discipline is the same; the measurement is harder. There is no label to compare against, so quality is judged by evaluation sets and graders — and cost per request matters in a way it never did for a classifier.

Do we need a feature store?

Only if training and serving are computing features separately, which is where skew comes from. With one pipeline doing both, a feature store is machinery you do not need yet.

How do we detect drift?

Monitor input distribution and output distribution separately. Inputs shifting means the world changed; outputs shifting without inputs shifting usually means something in the pipeline did.

Can you work with our existing tooling?

Yes, and usually better than replacing it. Most gaps are missing practices rather than missing tools — a registry nobody writes to does not help.

Who owns the code?

You do, from the first commit — work happens in your repository under your licence, and the contract assigns IP outright. We keep no rights and build no dependency that makes leaving expensive.

What does it cost?

We do not publish a number, because the honest one depends on scope, integrations and the accuracy bar. Tell us the budget you are working with and we will say what it buys — or say plainly if it does not buy enough.

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