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Building a Manufacturing Product Defect Classification Model and Application using H2O Hydrogen Torch, H2O MLOps, and H2O Wave
Primary Authors: Nishaanthini Gnanavel and Genevieve Richards Effective product quality control is of utmost importance in the manufacturing industry. The presence of defective components can have adverse effects on various aspects, including escalating production costs, compromising product quality, diminishing product longevity, and l...
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Improving Manufacturing Quality with H2O.ai and Snowflake
Manufacturers are rapidly expanding their machine learning use cases by leveraging the deep integration between Snowflake’s Data Cloud and the H2O AI Cloud. Many current manufacturing quality checks require that sensor data and image data be processed and analyzed separately. Standard tooling presents challenges in storing and referencin...
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AI-Driven Predictive Maintenance with H2O AI Cloud
According to a study conducted by Wall Street Journal , unplanned downtime costs industrial manufacturers an estimated $50 billion annually. Forty-two percent of this unplanned downtime can be attributed to equipment failure alone. These downtimes can cause unnecessary delays and, as a result, affect the business. A better and superior al...
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AI for Smarter Manufacturing
Code 3Manufacturing is a centuries old industry and has seen significant changes dating back to the first Industrial Revolution in the late 18th century. The use of conveyor belt assembly lines to replace assembly workers, newer precision robot technologies to further reduce manufacturing time, advances in ERP, historian databases, stora...
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Hortifrut uses AI to Determine the Freshness of Blueberries
Who doesn’t love sweet, delicious blueberries?Providing a steady supply of beautiful, tasty berries to the market is no small effort and Hortifrut, based in Chile, has been growing and distributing berries for the last 30 years. Today, they are using AI to provide fresh berries to the world everyday.Hortifrut, the largest global producer ...
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