MLOps & LLMOps Services for Production Model Operations

MLOps covers the full lifecycle of any model: training pipelines, a registry, deployment, and monitoring. LLMOps is the same discipline adapted to large language models, where the concerns shift to prompt and version management, token cost, evaluation of open-ended output, and guardrails. Most teams need both, because they run classic models and LLMs side by side.

A notebook has no versioning, no automated tests, no rollback, and no monitoring. In production a model faces changing data, latency limits, and cost ceilings that a notebook never checks. MLOps adds the registry, evaluation gates, serving layer, and telemetry that turn a working experiment into a system you can operate and trust.

For LLMs we use vLLM for high-throughput GPU serving with paged attention, and KServe for standard model endpoints with autoscaling. We route simple requests to smaller models and reserve large models for hard cases. The goal is meeting the latency target at the lowest cost per request, measured, not assumed.

We monitor input drift, output quality, latency, and cost, with alerts on thresholds set from a baseline. Drift on the input data is usually the first warning that accuracy is about to fall. Without monitoring, teams find out from customer complaints, which is the most expensive way to learn a model has drifted.

Yes. Every model change runs through automated evaluation against a fixed test set before it can be promoted, the same way code runs through tests. A model that fails the gate does not ship. We add canary or shadow deployment so a new model is validated on real traffic before it fully replaces the old one.

Usually yes. We build on your stack rather than replacing it, whether that is AWS SageMaker, GCP Vertex AI, Azure ML, or open-source MLflow and KServe. We add the missing pieces, a registry, gates, monitoring, rather than forcing a migration. A rebuild is proposed only when the current setup blocks reliable operation.

We right-size serving, autoscale GPUs to demand, batch requests where latency allows, and route by difficulty so small models handle easy traffic. Cost per request is tracked as a monitored metric, not reviewed once a quarter. Idle GPU capacity is the largest hidden cost in most LLM deployments, so we watch it continuously.

A feature store keeps the transformations that feed a model consistent between training and serving, which prevents a common bug where a model behaves differently live than in testing. You need one when several models share features or when training and serving pipelines have drifted apart. For a single simple model, it can be overkill, and we will say so.

We can hand over to your team with documentation and runbooks, run it under a support retainer, or embed engineers to operate it with you. We set up the monitoring and rollback paths so on-call is manageable rather than heroic. Ownership of the pipelines, registry, and infrastructure stays with you.