Towards Data Science is a community-powered publication that showcases work in data science, machine learning and artificial intelligence. Every day newcomers, seasoned researchers and industry practitioners publish tutorials, research notes and real-world case studies that help the field move forward.

Towards Data Science

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.

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

1. Leaving the Sandbox: The Strategy of Availability

2. The Monitoring Challenge And Why traditional metrics die at scale