Research area 06
Big data, analytics, and ML systems engineering
The infrastructure beneath the models, from data lakes and warehouses to serving, CI/CD, and observability, built so that research results can become dependable systems.
- data-engineering
- mlops
- cloud
- observability
A research result that cannot be rebuilt, redeployed, and monitored is a demo. Systems engineering is where LAISER’s research meets the real world: ingestion and curation pipelines, warehouses and feature tables, serving layers, and the continuous integration that keeps all of it honest.
We favour boring, well-understood infrastructure, infrastructure as code, deploys that only happen through a pipeline, and telemetry that is wired in from the first commit.
What we work on
- Data platforms. Lakes, warehouses, and registries that preserve raw artefacts and lineage.
- Delivery. Pipelines with protected branches, automated checks, and reproducible releases.
- Observability. Traces, metrics, and logs from both the application and the pipeline that built it, using open standards such as OpenTelemetry.