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Course Outline
Day 1: 09:00 - 16:00 (7h)
Foundations of Artificial Intelligence
- Defining AI, machine learning, and deep learning.
- Learning paradigms: supervised, unsupervised, and reinforcement.
- Distinguishing between AI myths and industrial realities.
AI Within Smart Manufacturing Contexts
- Defining the characteristics of a “smart” factory.
- The contribution of AI to Industry 4.0 and industrial automation.
- A look at enabling technologies such as IoT, edge computing, and digital twins.
Critical Manufacturing Applications
- Predictive maintenance and ensuring equipment reliability.
- Quality assurance through anomaly detection.
- Optimizing processes to improve yield.
Navigating the Data Lifecycle
- Sensing and gathering industrial data.
- Data preparation and quality management.
- Core principles of data-informed decision making.
Day 2: 09:00 - 16:00 (7h)
Strategic AI Project Planning
- Pinpointing high-impact use cases.
- Assembling the right team and defining success metrics.
- Addressing common challenges and mitigation tactics.
Case Studies and Sector-Specific Applications
- Real-world examples from automotive, food, pharmaceutical, and heavy industry sectors.
- Insights gained from digital transformation experiences.
- Key success factors and common pitfalls to avoid.
Getting Started Roadmap
- Steps to initiate an AI initiative.
- Technology evaluation and vendor selection criteria.
- Considerations for scalability, ethics, and workforce adaptation.
Recap and Future Directions
Requirements
- Familiarity with basic industrial processes or plant operations
- Curiosity regarding digital transformation and innovation strategy
- Openness to discussions on technology adoption
Target Audience
- Operations managers
- Plant executives
- Technical leads
14 Hours
Testimonials (2)
All in general
Daniele Donzelli - ITT ITALIA S.r.l.
Course - CANoe for CAN Compact Training
PLC basic knowledge
