Course Outline
Introduction to Edge AI in Autonomous Systems
- Overview of Edge AI and its significance in autonomous systems.
- Key benefits and challenges of implementing Edge AI in autonomous systems.
- Current trends and innovations in Edge AI for autonomy.
- Real-world applications and case studies.
Real-Time Processing in Autonomous Systems
- Fundamentals of real-time data processing.
- AI models for real-time decision making.
- Handling data streams and sensor fusion.
- Practical examples and case studies.
Edge AI in Autonomous Vehicles
- AI models for vehicle perception and control.
- Developing and deploying AI solutions for real-time navigation.
- Integrating Edge AI with vehicle control systems.
- Case studies of Edge AI in autonomous vehicles.
Edge AI in Drones
- AI models for drone perception and flight control.
- Real-time data processing and decision making in drones.
- Implementing Edge AI for autonomous flight and obstacle avoidance.
- Practical examples and case studies.
Edge AI in Robotics
- AI models for robotic perception and manipulation.
- Real-time processing and control in robotic systems.
- Integrating Edge AI with robotic control architectures.
- Case studies of Edge AI in robotics.
Developing AI Models for Autonomous Applications
- Overview of relevant machine learning and deep learning models.
- Training and optimizing models for edge deployment.
- Tools and frameworks for autonomous Edge AI (TensorFlow Lite, ROS, etc.).
- Model validation and evaluation in autonomous settings.
Deploying Edge AI Solutions in Autonomous Systems
- Steps for deploying AI models on various edge hardware.
- Real-time data processing and inference on edge devices.
- Monitoring and managing deployed AI models.
- Practical deployment examples and case studies.
Ethical and Regulatory Considerations
- Ensuring safety and reliability in autonomous AI systems.
- Addressing bias and fairness in autonomous AI models.
- Compliance with regulations and standards in autonomous systems.
- Best practices for responsible AI deployment in autonomous systems.
Performance Evaluation and Optimization
- Techniques for evaluating model performance in autonomous systems.
- Tools for real-time monitoring and debugging.
- Strategies for optimizing AI model performance in autonomous applications.
- Addressing latency, reliability, and scalability challenges.
Innovative Use Cases and Applications
- Advanced applications of Edge AI in autonomous systems.
- In-depth case studies in various autonomous domains.
- Success stories and lessons learned.
- Future trends and opportunities in Edge AI for autonomy.
Hands-On Projects and Exercises
- Developing a comprehensive Edge AI application for an autonomous system.
- Real-world projects and scenarios.
- Collaborative group exercises.
- Project presentations and feedback.
Summary and Next Steps
Requirements
- A solid understanding of AI and machine learning concepts.
- Experience with programming languages (Python is recommended).
- Familiarity with robotics, autonomous systems, or related technologies.
Audience
- Robotics engineers.
- Autonomous vehicle developers.
- AI researchers.
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 2600 € + VAT*
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