From the lab to production
Training data governance, annotation QA, feature store and CI/CD/CT pipelines
Post-launch problems concentrate in data and process: unversioned training data, inconsistent annotation, manual deployment, unmonitored drift. We build training data governance and annotation QA, set up a feature store, experiment tracking and model registry, connect CI / CD / CT pipelines, and deploy monitoring, drift detection and compute scheduling.
Our approach
One team scopes, executes and retests; conclusions are delivered as evidence, not checklists.
Datasets are versioned like models
Every training run traces to a dataset version, code and parameters; results are reproducible.
Pipelines start minimal
The automated path from training to evaluation to deployment comes first; feature store and advanced features follow once it is stable.
Monitoring metrics defined before launch
Thresholds for data drift, prediction drift and business metrics are set before deployment; monitoring starts at go-live.
Deliverables
Training data governance and annotation system
Dataset versioning, annotation standard and QA process, annotation platform configuration.
MLOps platform
Feature store, experiment tracking, model registry, CI/CD/CT pipelines.
Monitoring and operations manual
Drift detection, alerting, compute scheduling policy and operating procedures.
How we deliver
Four stages, each with defined inputs, outputs and a client sign-off.
Assessment
Existing models, data process, toolchain and pain points.
Platform design
Architecture, tool selection, pipeline design.
Build and migration
Platform set-up, first models onto the pipeline.
Handover
Monitoring live, operations training, documentation.
Frequently asked questions
Notes on scope, execution and delivery standards. Contact us for anything not covered here.
Kubernetes or equivalent orchestration, object storage, a code repository and CI tooling. It deploys on public cloud or private environments and supports domestic GPUs and the domestic software stack.
Start from where you stand
Security, data and AI each start with a review of where you stand. The report and its findings are yours, whether or not the engagement continues.