Editing Ai Infrastructure Mlops (section)

== <span style="color: #FFFFFF;">Evaluating</span> ==
Expert MLOps evaluation assesses the entire system, not just the model:

'''Deployment reliability''': What is the rollback time if a new model causes a regression? Blue/green and canary deployments allow traffic shifting with instant rollback.

'''Latency SLA compliance''': Track p50/p95/p99 inference latency. Alert if p95 exceeds target. Profile inference to identify bottlenecks (tokenization, model forward pass, post-processing).

'''Data pipeline health''': Feature pipeline reliability (what % of features are computed on time?), data freshness lag, schema validation failure rates. Upstream data quality directly impacts model quality.

'''Model decay tracking''': Plot key prediction metrics (PSI for probability distribution shift, prediction mean, business KPIs) over time. Statistical tests (Kolmogorov-Smirnov, Population Stability Index) detect distribution drift before it degrades business metrics.

Expert practitioners define '''SLOs (Service Level Objectives)''' for ML systems just as they would for any software service: model accuracy > X% (monitored weekly), prediction latency p95 < Yms, feature pipeline freshness < Z minutes.
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