IndustryWeek: Machine Learning Hurdles Are High, But Should Lower Quickly
This article originally appeared in IndustryWeek.
A new wave of automation is projected to revolutionize the manufacturing industry, with organizations around the world investing in machine learning and artificial intelligence (AI) solutions aimed at improving every phase of production. Manufacturers aim to produce high-quality products at minimum cost, and many see machine learning as a way to streamline production, improve product quality, increase employee safety and more.
While still early in the adoption cycle, machine learning is already playing a role in reducing unplanned machinery downtime, and is expected to make a large impact on predictive maintenance as well. It enables manufacturers to quickly identify anomalies to prevent failures and breakdowns, which is extremely valuable to businesses. In fact, McKinsey predicts that AI-based predictive maintenance will deliver between $500B to $700B in value to manufacturers.
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Jeff Fried, Director of Product Management for InterSystems, is a long-standing data management nerd, and particularly passionate about helping people create powerful data-driven applications. Prior to joining InterSystems, Jeff served as CTO of BA Insight, Empirix, and Teloquent, and ran product management for FAST Search and Transfer and for Microsoft. He has extensive experience in data management, text analytics, enterprise search, and interoperability. Jeff is a frequent speaker and writer in the industry; holds 15 patents; and has authored more than 50 technical papers and co-authored three technical books.