AI Systems Implementation and Operations
From pilot to production
Candidates should understand what changes when a model moves into production: integration with applications and data pipelines, latency and throughput constraints, scalability, and cost management. Know the components of an operational AI stack - data ingestion, feature pipelines, model serving, monitoring, and logging. Understand MLOps concepts: versioning of data, code, and models; CI/CD and continuous training; reproducibility; and rollback capability. Be able to explain monitoring dimensions: performance drift, data drift, concept drift, and system health. Know why human oversight, alerting, and incident response are operational controls, and how shadow deployment and canary releases reduce risk.
Traps that appear in questions
A common trap is treating deployment as the finish line; most failures appear after launch as data distributions shift. Another is confusing data drift (input distribution changes) with concept drift (the input-output relationship changes). Questions may assume a model validated offline will perform identically online, ignoring feedback loops and pipeline skew between training and serving features. Watch for monitoring plans that track only uptime and latency while ignoring model quality, and for missing rollback paths.
Worked example / rule of thumb
Rule of thumb: monitor inputs, outputs, and outcomes - not just infrastructure. Example: a recommendation model's servers can be healthy while click-through collapses because user behavior shifted (concept drift); detecting this requires tracking outcome metrics and retraining triggers, not only CPU and latency dashboards.