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

Managing a large portfolio of machine learning models requires shifting from a sandbox mindset to an infrastructure-focused strategy. This approach prioritizes system availability, using safe fallbacks to ensure the product remains online even if a model drifts or fails. Traditional monitoring metrics like accuracy are often insufficient at scale, necessitating a focus on robust engineering, tiered hardware strategies, and careful prevention of issues like label leakage. To ensure reliability, safety nets such as shadow deployments and human-in-the-loop auditing are essential before and after models go live. Ultimately, the challenge is not just deploying a model, but ensuring a massive portfolio works reliably and safely.