Machine Learning at Scale: Managing More Than One Model in Production

Managing a portfolio of ML models in production requires a fundamentally different mindset than single-model deployments. Key challenges include prioritizing availability over perfection (using safe fallbacks when models fail), the limitations of traditional accuracy metrics at scale, infrastructure decisions around cloud vs. device and tiered GPU/CPU strategies, and the near-invisible risk of label leakage across complex data pipelines. Practical safeguards include feature latency monitoring, shadow deployments, and human-in-the-loop auditing for high-stakes models.