By Mohamed Abuali and Dr. David Siegel
Less than successful machine learning (ML) or artificial intelligence (AI) projects fall short because they fail to incorporate domain knowledge, suffer from poor data quality, deliver an inadequate business case or struggle with models lacking robustness.
Attend this webcast to learn how these challenges can be addressed. We’ll also review real-world case study examples in predictive maintenance that focus on reducing unplanned downtime as well as others concerned with predictive quality and reducing scrap, including for processes involving stamping, casting, CNC machining and industrial robots.
The presentation and case studies shed the light on how manufacturers can transition from a “fail-and-fix” to a “predict-and-prevent” zero-downtime and zero-defect operation.
- How to define the business and technical problems involved
- An end-to-end analysis platform for data collection
- Lessons on how solutions can be maintained and improved over time.