ML model accuracy is only

Short summary

Model accuracy represents only ~10% of shipping ML systems; the other 90% is engineering around feature drift, data monitoring, and serving constraints like latency and cost. Teams create real value by using shared feature stores, implementing automated retraining pipelines with quality gates, and optimizing for business outcomes—not proxy metrics like accuracy alone.

• •Model accuracy is just the foundation; 90% of ML production work is engineering around data drift, monitoring, and serving constraints
• •Shared feature stores, automated retraining pipelines, and data-drift monitoring prevent silent failures that tank production models
• •Success requires optimizing for real business metrics (fraud caught net of support costs, revenue impact) rather than proxy metrics like accuracy

Generated with AI, which can make mistakes.