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Engineering for Robust Quality in AI Systems As AI systems transition from proof-of-concept to production-grade showcases, the challenge shifts from initial model development to sustaining and enhancing their performance, reliability, and efficiency at scale. It's not enough to have a performant model; it must be consistently performant, easily deployable, and cost-effective. This demands a disciplined engineering approach, integrating MLOps principles from the outset. Establishing a Baseline: Metrics and Observability The foundation of maintaining quality is a clear definition of what 'quality' means for your specific AI application. Beyond traditional accuracy or F1-score, consider metrics like inference latency, throughput, model fairness,...