Lifecycle
Data, features, experiments, models, approvals and deployment.
MLOps & AI security
MLOps consulting and engineering
Build reproducible paths for data, training, evaluation, model release and production operation. Ship and operate model-backed systems with clear data, deployment and monitoring boundaries.
01 / Scope
Work on the system, not around it.
The engagement starts with the architecture and working constraints already in place. We agree the risky paths, make the changes visible and leave ownership with the team running the system.
Reproducibility
Code, environment, data lineage, parameters and artifacts.
Delivery
Evaluation gates, registry, release, rollback and access.
Operations
Monitoring, retraining, incident response and cost.
02 / Method
Ship and operate model-backed systems with clear data, deployment and monitoring boundaries.
Scope and access are agreed before the work begins. Decisions, changes and residual risk remain visible throughout the engagement.
Trace data and model flow
Start with evidence, owners and the constraints that shape the safe solution.
Control the release path
Prioritise by practical impact and implement with the people who operate the system.
Observe behaviour in production
Test the changed path, document the decision and hand over a backlog that still makes sense.
03 / What remains
- 01ML lifecycle and ownership map
- 02Reproducible training workflow
- 03Model registry and deployment path
- 04Monitoring and operating runbook
Related work
Continue with the adjacent problem.
04 / Talk to Gritt
What is happening in the system?
Send the cloud, workload or deadline. An engineer will reply and tell you whether this is the right scope.
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