MLOps.

Technologies

• AI system lifecycle processes Defined stages from concept to retirement, each with owners and records.
• Data quality management Measuring and managing completeness, accuracy, representativeness and timeliness of training and input data.
• Experiment tracking and reproducibility Recording code, data, parameters and environment so any model can be rebuilt.
• Continuous training and deployment to edge fleets Pipelines that retrain, test and roll models out to sites in stages.
• Drift and performance monitoring Watching input and output distributions and field accuracy, with triggers for review or rollback.
• Model and dataset bills of materials Inventories of models, datasets and dependencies for each deployed AI system.
• Model provenance and signing Signed, traceable model artefacts and labelled AI-generated content.