MLOps Consulting Services
The model is the easy part. We build the operational discipline around it — evaluation, deployment, observability, and the workflows that let your team run AI systems reliably over time.
Operations is where AI lives or dies
Most AI value is lost after deployment: models drift, prompts regress, costs creep, and no one notices until a user does. MLOps — and increasingly LLMOps — is the discipline that prevents that.
We build the evaluation, deployment, and observability layers that turn a working model into a system your team can operate, monitor, and improve with confidence.
What we set up
- —Evaluation pipelines and offline/online test suites for models and LLM systems.
- —Deployment paths — versioning, rollout, and rollback for models and prompts.
- —Observability — quality, latency, cost, and drift monitoring with alerting.
- —Data and feedback loops that feed real usage back into evaluation.
- —Runbooks and handover so your team owns operations confidently.
MLOps and LLMOps
Classical MLOps and LLM operations share principles but differ in practice — evaluation for generative systems is harder, and cost and latency behave differently. We work across both, and meet you wherever your stack is today rather than imposing a tool we happen to like.
What you get
Deliverables
FAQ
MLOps Consulting: common questions
What is MLOps?
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MLOps is the set of practices for deploying, monitoring, and maintaining machine-learning systems reliably in production — covering evaluation, deployment, versioning, observability, and the feedback loops that keep models accurate over time.
What is the difference between MLOps and LLMOps?
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LLMOps applies operational discipline specifically to large language model systems. It shares MLOps principles but adds challenges unique to generative AI — harder evaluation, prompt and context versioning, and different cost and latency dynamics.
Do you work with our existing tools?
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Yes. We are tool-agnostic and build on the stack you already use wherever possible, rather than forcing a migration. The goal is operational discipline your team can sustain, not a new set of tools to learn.
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