AI in Manufacturing: Predictive Maintenance and Alerts

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This hands-on, project-based course teaches you how to predict machine failures before they happen by combining machine learning with real-time, no-code automation. You'll move from the business case for predictive maintenance — the crippling cost of unplanned downtime and why reactive maintenance is broken — through the sensor data and failure signatures that make prediction possible, and into a plain-English primer on the ML models behind it all, with no heavy maths required. In two guided hands-on sessions, you'll build and train a Random Forest failure-detection model in Google Colab on the AI4I 2020 dataset, then wire it into a live n8n workflow that simulates sensor data, calls your model's API, routes decisions with an IF node, and fires real email alerts the moment a failure is predicted. You'll finish by exploring real deployment challenges — sensor drift, alert fatigue, and ERP/CMMS integration — and preview where to go next, from binary "will it fail?" detection to RUL regression that answers "when will it fail?". The course spans 3 hours total: 100 minutes of theory, 60 minutes of hands-on building, and a 20-minute wrap-up.This hands-on, project-based course teaches you how to predict machine failures before they happen by combining machine learning with real-time, no-code automation. You'll move from the business case for predictive maintenance — the crippling cost of unplanned downtime and why reactive maintenance is broken — through the sensor data and failure signatures that make prediction possible, and into a plain-English primer on the ML models behind it all, with no heavy maths required. In two guided hands-on sessions, you'll build and train a Random Forest failure-detection model in Google Colab on the AI4I 2020 dataset, then wire it into a live n8n workflow that simulates sensor data, calls your model's API, routes decisions with an IF node, and fires real email alerts the moment a failure is predicted. You'll finish by exploring real deployment challenges — sensor drift, alert fatigue, and ERP/CMMS integration — and preview where to go next, from binary "will it fail?" detection to RUL regression that answers "when will it fail?". The course spans 3 hours total: 100 minutes of theory, 60 minutes of hands-on building, and a 20-minute wrap-up. Independent Course Disclaimer Disclaimer: This is an independent educational resource created by Board Infinity for informational and educational purposes only. This course is not affiliated with, endorsed by, sponsored by, or officially associated with any company, organization, or certification body unless explicitly stated. The content provided is based on industry knowledge and best practices but does not constitute official training material for any specific employer or certification program. All company names, trademarks, service marks, and logos referenced are the property of their respective owners and are used solely for educational identification and comparison purposes.

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