AI Project Delivery and Operations
AI delivery blends product management, data engineering, and MLOps. The lifecycle starts with a problem statement tied to a metric, not a model choice. Use feasibility assessment (data availability, label quality, baseline) before committing. Prefer a simple baseline (rules or logistic regression) to establish whether complexity earns its keep.
MLOps operationalizes models: versioned data and code, reproducible training, automated testing, CI/CD for models, model registry, deployment patterns (batch, real-time API, streaming, edge), and monitoring for latency, errors, data drift, concept drift, and output quality. Define retraining triggers (scheduled or drift-based).
Delivery methods: waterfall (fixed scope) versus agile/iterative (fits AI's uncertainty). A pilot or proof of concept validates feasibility, not production readiness; scaling requires integration, security review, and change management. Track KPIs (business outcomes) and model metrics separately. Plan rollback and incident response for model failures, and maintain human-in-the-loop where risk demands.
Traps: treating a successful notebook as production-ready; ignoring data pipelines and monitoring; assuming retraining always improves performance (it can regress without validation gates); or conflating model accuracy with user adoption.
Worked example: a demand-forecast model goes live in batch. Monitoring shows input drift in a key feature and rising forecast error. The retraining trigger fires; the new model is validated against a holdout and shadow-deployed before promotion. Rollback to the prior version is available if error exceeds threshold.
Rule of thumb: a model is not delivered until it is monitored, versioned, and revertible; budget for operations, not just build.