Signals
Service health, data quality, drift, performance and safety indicators.
MLOps & AI security
ML model monitoring engineering
Monitor model inputs, outputs, performance and operational health with owners for meaningful change. 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.
Baselines
Segments, seasonality, uncertainty and expected change.
Response
Owners, thresholds, investigation and rollback or retraining.
Governance
Dashboards, evidence, retention and review cadence.
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
- 01Monitoring and ownership design
- 02Instrumentation and quality checks
- 03Dashboards and actionable alerts
- 04Response and retraining 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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