Modern AI Development: From Prototype to Production

Data, models, evaluation, and deployment

Successful AI projects begin with well-scoped problems and clean data. Prioritize labeling quality and define metrics tied to business outcomes.

Choose the right model for the job: classical ML for structured data, deep learning for vision and speech, and LLMs for generative tasks.

Evaluation must be continuous. Track accuracy and robustness, but also latency, cost, and fairness. Create test sets that mirror real-world drift.

Productionizing AI requires observability. Collect feature and prediction traces, monitor performance, and set up rollback strategies.

Ship with MLOps practices: version datasets, models, and configs; automate training pipelines; and document interfaces for maintainability.