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Course Outline
Day 1: 09:00 - 16:00 (7h)
Fundamentals of Artificial Intelligence
- Defining AI, machine learning, and deep learning.
- Learning paradigms: supervised, unsupervised, and reinforcement.
- Clarifying myths and realities of industrial AI.
AI in Smart Manufacturing Contexts
- Defining the attributes of a “smart” factory.
- AI’s contribution to Industry 4.0 and industrial automation.
- Overview of supporting technologies (IoT, edge computing, digital twins).
Key Manufacturing Applications
- Predictive maintenance and equipment reliability.
- Quality assurance and anomaly detection.
- Process optimization and yield enhancement.
Navigating the Data Lifecycle
- Sensing and gathering industrial data.
- Data preparation and quality standards.
- Core concepts in data-driven decision-making.
Day 2: 09:00 - 16:00 (7h)
AI Project Planning and Strategy
- Identifying high-impact use cases.
- Assembling the right team and defining success metrics.
- Addressing common challenges and mitigation strategies.
Case Studies and Industry Applications
- Real-world examples from automotive, food, pharmaceutical, and heavy industries.
- Insights from digital transformation journeys.
- Key success factors and pitfalls to avoid.
Getting Started Roadmap
- Steps to launch an AI initiative.
- Technology considerations and vendor selection.
- Scalability, ethics, and workforce adaptation.
Recap and Next Steps
Requirements
- Familiarity with basic industrial processes or plant operations.
- Interest in digital transformation or innovation strategies.
- Openness to discussing 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
