Machine learning models need love, too

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A shining city on a hill is a sight to behold. But you wouldn’t admire it so much if the city stopped maintaining its roads, electrical blackouts grew more frequent, electricity grew intermittent, and those gorgeous buildings started to fade under thick coats of grime.

Modern businesses are building their shiny new applications on a foundation of machine learning. For any organization that hopes to automate distillation of patterns in feeds of big data, natural language, streaming media, and Internet of things sensor data, there’s no substitute for machine learning. But these data-analysis algorithms, like the glimmering city, will decay if no one is attending to their upkeep.

Machine learning algorithms don’t build themselves — and they certainly don’t maintain themselves. Where model building is concerned, you probably have your best and brightest data scientists dedicated to the responsibility. Therein lies a potential problem: You may have far fewer data-scientist person-hours dedicated to the unsexy task of maintaining the models you’ve put into production.

Read the source article at InfoWorld