AI Solution Development and Deployment
Architecture and serving
Know the components: data pipelines, feature stores, training jobs, model registry, serving layer, and monitoring. Distinguish batch inference (scheduled scoring of large datasets) from real-time inference (low-latency API calls). Understand containerization, APIs, and the role of a model registry for versioning and rollback.
Deployment strategies
Canary releases send a small share of traffic to a new model; blue-green swaps environments; shadow mode runs a new model alongside the old without affecting users, comparing outputs. A/B testing measures business impact with controlled exposure. Know when each applies.
MLOps and monitoring
MLOps applies CI/CD ideas to models: automated testing, reproducible training, versioned data and artifacts, and continuous monitoring. Monitor input distributions, prediction distributions, latency, errors, and outcome metrics. Alerts on drift trigger investigation and retraining. Track lineage so any prediction can be traced to data and model versions.
Reliability and cost
Consider fallbacks when a model or dependency fails, rate limits, caching, and cost per inference. Guardrails filter unsafe or malformed outputs before they reach users.
Worked example
A team deploys a new ranking model. They first run it in shadow mode to compare rankings without user impact, then a 5% canary with latency and engagement monitoring, then full rollout with automated rollback on error-rate thresholds.
Rule of thumb
Never let a model reach all users without a rollback path and a monitoring signal tied to the business outcome.