--- title: Machine Learning at Scale: Managing More Than One Model... description: Managing a portfolio of ML models in production requires a fundamentally different mindset than single-model deployments. Key challenges include prioritizing... canonical: https://daily.dev/posts/machine-learning-at-scale-managing-more-than-one-model-in-production-r2wbzp29a twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Machine Learning at Scale: Managing More Than One Model in Production | daily.dev og:description: Managing a portfolio of ML models in production requires a fundamentally different mindset than single-model deployments. Key challenges include prioritizing... og:url: https://daily.dev/posts/machine-learning-at-scale-managing-more-than-one-model-in-production-r2wbzp29a og:image: https://api.daily.dev/og/posts/r2wBzp29a.png og:image:alt: Machine Learning at Scale: Managing More Than One Model in Production og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Machine Learning at Scale: Managing More Than One Model in Production **[Towards Data Science](https://daily.dev/sources/tds)** · 7 min read · 19 upvotes · 0 comments ## Summary 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. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [Why Most Machine Learning Projects Fail to Reach Production](https://daily.dev/posts/why-most-machine-learning-projects-fail-to-reach-production-opcx6gdce) · InfoQ · 0 upvotes · 0 comments --- Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#mlops](https://daily.dev/tags/mlops) [View this post on daily.dev](https://daily.dev/posts/machine-learning-at-scale-managing-more-than-one-model-in-production-r2wbzp29a) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Machine Learning at Scale: Managing More Than One Model in Production","url":"https://daily.dev/posts/machine-learning-at-scale-managing-more-than-one-model-in-production-r2wbzp29a","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/machine-learning-at-scale-managing-more-than-one-model-in-production-r2wbzp29a"},"datePublished":"2026-03-09T12:39:08.481Z","dateModified":"2026-03-09T12:39:34.250Z","description":"Managing a portfolio of ML models in production requires a fundamentally different mindset than single-model deployments. 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