MLOps and LLMOps that get models and LLM apps to production and keep them there.

Cost depends on how many models and LLM apps move onto the pipeline, which cloud and tools you already run, how much data needs versioning and how strict your audit rules are. Teams with Kubernetes and CI/CD in place need less setup than teams starting fresh. Cloud and GPU usage are billed to your own accounts. You'll get a written scope after a free call.

MLOps covers models you train yourself, such as forecasting, classification or computer vision models, with data pipelines, training, a registry, deployment and drift monitoring. LLMOps covers apps built on large language models, where you also version prompts and retrieval settings, score outputs with evaluation sets and track token cost and latency. Most of the plumbing is shared, so we set up one pipeline that handles both.

Private AI hosting chooses an open-weight model and runs it on your own infrastructure. Managed AI agents is a team that watches a live agent's conversations and tunes it month by month. MLOps and LLMOps is the release system underneath both: the pipelines, registry, evaluation gates, deployment and rollback that every model or prompt change passes through, run by your own team.

We use MLflow for experiment tracking and the registry, Kubeflow, SageMaker Pipelines or Vertex AI for pipelines, vLLM or KServe for serving, and Evidently AI, Grafana and Langfuse for monitoring. If you already run a platform such as Azure Machine Learning or Databricks, we build inside it. The choice follows your cloud and your team's skills.

An evaluation gate is an automatic step in your release pipeline that scores a new model or prompt on your test sets and compares it with the version in production. A candidate that meets the agreed bar on quality, latency and cost moves to a canary release. One that falls short stays in staging, with a report showing where it scored lower.

We compare live inputs and predictions with the data the model was trained and tested on, using statistical drift checks, and track real quality wherever true outcomes arrive later, such as a forecast against actual sales. For LLM apps we sample outputs and score them against evaluation sets. When a threshold is crossed, your team gets an alert and a retraining run can start automatically.

Rollback is a single step, because the previous version stays registered and ready to serve. Canary releases send a small share of traffic to the new version first, so most problems show up on a fraction of requests, and switching back is one command or one click in the pipeline.

Yes, the pipeline is built for your team to use every day. Data scientists keep their notebooks and frameworks, and we turn their work into versioned pipelines while pairing with them. Everything lives in your repositories and cloud accounts, with documentation and training at handover.

Access to the code, data and cloud accounts behind your current models and LLM apps, one model to move first, and someone who can define what good output looks like for it. A platform or DevOps contact helps us fit the pipeline to your existing CI/CD and security rules.