Services
MLOps / LLMOps & AI Engineering
Move models and AI applications from prototype to reliable, observable, maintainable operations.
The business challenge
A prototype does not automatically become a reliable service. Lumera structures packaging, deployment, monitoring, drift detection, and rollback.
What Lumera delivers
- Reproducible environments and versioned artifacts.
- Inference APIs, registries, and controlled deployment workflows.
- Data, model, and application observability with rollback strategy.
Core capabilities
- Model deployment and inference APIs
- Registry, monitoring, and data/model drift detection
- LLM evaluation and prompt/version management
- CI/CD, retraining, and canary or shadow strategies
Example use cases
- Production deployment of a predictive model
- Monitored inference service
- Evaluation and versioning of an LLM application
- Controlled retraining workflow
Technologies we work with
MLflow
Docker
Kubernetes
GitHub Actions
Prometheus
Grafana
Python
Delivery approach and architecture
- 01Develop
- 02Validate
- 03Package
- 04Deploy
- 05Monitor
- 06Detect drift
- 07Version / rollback
Engineering practices
- Reproducible environments and immutable version history
- Automated tests, staging, and progressive deployment
- Monitoring, rollback, and explicit artifact management
Security and production readiness
- Secrets management, RBAC, environment isolation, and API authentication.
- Container scanning, versioned artifacts, and deployment logging.