lloT Edge/Cloud series, Part 4: Machine learning and pattern recognition
How machine learning is being used in real-world applications
ONE (1) CERTIFIED PROFESSIONAL DEVELOPMENT HOUR (PDH) AVAILABLE FOR ALL ATTENDEES.
AI-based machine learning algorithms can automatically monitor and analyze quality issues including detect, identify, predict and prevent quality issues.
The actionable intelligence derived from these data insights enhances business operations by optimizing production processes, reducing costs and wasted scrap, and preventing warranty claims and recalls.
In this webcast, we’ll examine the use of machine learning and pattern recognition in predictive quality solutions through several automotive and food & beverage industry case studies, noting that they address common industry pain points. We’ll also look at what kinds of technology, infrastructure and data will be needed to get started in each area.
Learning objectives:
- Learn how machine learning and pattern recognition are being incorporated into predictive quality processes.
- Understand the value of predictive quality solutions for process optimization, including return on investment through cost and scrap reduction and prevention of warranty claims and recalls.
- Configuration of predictive quality solutions, including common industry pain points, infrastructure and data needed to get started.
Presented By:
Mohamed Abuali, President, IoTco
Dr. David Siegel, Chief Technology Officer, Predictronics Corp.
Moderated By:
Kevin Parker, Content Manager, CFE Media and Technology
