Among the wealth of use cases for AI & ML in manufacturing, one rises above the rest in terms of feasibility and impact – predictive maintenance. Predictive maintenance addresses the age-old challenge of ensuring maximum availability of critical manufacturing systems.
Webinar from | RapidMiner
Amidst the Industry 4.0 revolution, manufacturers are embracing cyber-physical systems and predictive analytics to drive astounding levels of innovation. In fact, across all industries, manufacturing is expected to generate 1/3 of the world’s investment in AI systems in 2019.
Competition is one reason for the rapid adoption of these technologies. In the current climate, those that don’t evolve quickly will likely find themselves outperformed by aggressive and nimble global competitors. Another reason is the tremendous amount of opportunity. Manufacturers can use AI systems to design smart products, run smart factories, forecast demand, ensure quality, reduce production downtime, and manage supply chain risk.
Among the wealth of use cases for AI & ML in manufacturing, one rises above the rest in terms of feasibility and impact – predictive maintenance. Predictive maintenance addresses the age-old challenge of ensuring maximum availability of critical manufacturing systems, while simultaneously minimizing the cost of maintenance and repairs.
Join RapidMiner for this 60-minute webinar, where they’ll cover:
- The variety of use cases for AI & ML in manufacturing
- Why you can’t wait any longer to roll out predictive maintenance
- How to use RapidMiner for predictive maintenance in your organization
The content & opinions in this article are the author’s and do not necessarily represent the views of ManufacturingTomorrow
RapidMiner brings artificial intelligence to the enterprise through an open and extensible data science platform. Built for analytics teams, RapidMiner unifies the entire data science lifecycle from data prep to machine learning to predictive model deployment. More than 500,000 analytics professionals use RapidMiner products to drive revenue, reduce costs, and avoid risks.
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