Production AI is an operating system for data, models and decisions. MLOps creates the repeatable path from a trusted dataset to a deployed model, while monitoring shows whether that model continues to behave as expected after the environment changes.
Make data quality observable
Track freshness, completeness, schema changes, duplicates, outliers and label availability. Data contracts should identify the owner of each critical field and define what happens when a pipeline violates an expectation.
Evaluate before and after deployment
Use representative, versioned datasets and task-specific metrics. After deployment, compare live outcomes with the baseline and watch for drift in inputs, predictions and business results. For GenAI, include groundedness, refusal quality and citation accuracy.
Connect technical signals to business signals
Latency, error rate and infrastructure cost are important, but not the whole scorecard. Pair them with conversion, resolution time, forecast error, quality review results or other measures of the decision the system supports.
The release gate
A production release should have a versioned artifact, an evaluation report, an owner, a rollback path and a monitoring dashboard that someone is responsible for reviewing.
