Your AI keeps changing. Who reviews it?

Keep experiments and production models separate

A notebook often contains implicit assumptions about files, libraries and manually executed steps. We turn the relevant processing steps into reproducible pipelines with recorded dependencies. Model artifacts receive a unique version and a reference to their training, configuration and evaluation.

Registration alone is not yet a release. We define which quality, security and operational checks must be passed before publication. Business assessment and technical deployment remain distinct, so that a technically working model does not go into production without a content review.

Detect degradation and respond in a controlled way

In operation, we monitor availability, response times and changes in the input data. A changed data distribution is a reason to investigate, but not yet proof of worse predictions. Where business outcomes only become known later, we plan for feeding them back into the quality assessment.

Model changes happen with an agreed comparison and fallback path. Depending on the use case, shadow evaluations or limited user groups can make sense at first. If a model is rolled back, its associated preprocessing and interfaces must also match up. Your operations team receives instructions for incidents, redeployment and the escalation of business anomalies.