Edge AI and Robotics: Enabling Autonomous Systems Training Course
Edge AI is revolutionizing robotics by enabling real-time decision-making in autonomous systems.
This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level robotics engineers, AI developers, and automation specialists who wish to implement Edge AI for robotics applications.
By the end of this training, participants will be able to:
- Understand the role of Edge AI in autonomous systems.
- Deploy AI models on edge devices for real-time robotics.
- Optimize AI performance for low-latency decision-making.
- Integrate computer vision and sensor fusion for robotic autonomy.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Course Outline
Introduction to Edge AI in Robotics
- What is Edge AI?
- Why Edge AI is essential for robotics
- Challenges of real-time AI in autonomous systems
Deploying AI Models on Edge Devices
- AI inference on NVIDIA Jetson and other edge hardware
- Using TensorFlow Lite and ONNX for edge deployment
- Optimizing AI models for real-time execution
Real-Time Perception for Autonomous Systems
- Computer vision for robotic navigation
- Sensor fusion: LiDAR, cameras, and IMUs
- Edge AI for object detection and tracking
Decision-Making and Control in Robotics
- Reinforcement learning for autonomous behaviors
- Path planning and obstacle avoidance
- Latency optimization in real-time AI systems
Integrating AI with ROS (Robot Operating System)
- Overview of ROS and its ecosystem
- Running AI-based perception models in ROS
- Edge AI in multi-robot and swarm robotics applications
Optimizing AI for Low-Power Robotic Systems
- Efficient neural network architectures for robotics
- Reducing power consumption in AI-driven robots
- Deploying AI on battery-powered robotic platforms
Real-World Applications and Future Trends
- Autonomous drones and industrial robots
- AI-powered robotic assistants
- Future advancements in Edge AI for robotics
Summary and Next Steps
Requirements
- An understanding of AI and machine learning models
- Experience with embedded systems or robotics
- Basic knowledge of real-time computing
Audience
- Robotics engineers
- AI developers
- Automation specialists
Open Training Courses require 5+ participants.
Edge AI and Robotics: Enabling Autonomous Systems Training Course - Booking
Edge AI and Robotics: Enabling Autonomous Systems Training Course - Enquiry
Edge AI and Robotics: Enabling Autonomous Systems - Consultancy Enquiry
Consultancy Enquiry
Upcoming Courses
Related Courses
Advanced Edge AI Techniques
14 HoursThis instructor-led, live training in Chile (online or onsite) is aimed at advanced-level AI practitioners, researchers, and developers who wish to master the latest advancements in Edge AI, optimize their AI models for edge deployment, and explore specialized applications across various industries.
By the end of this training, participants will be able to:
- Explore advanced techniques in Edge AI model development and optimization.
- Implement cutting-edge strategies for deploying AI models on edge devices.
- Utilize specialized tools and frameworks for advanced Edge AI applications.
- Optimize performance and efficiency of Edge AI solutions.
- Explore innovative use cases and emerging trends in Edge AI.
- Address advanced ethical and security considerations in Edge AI deployments.
Building AI Solutions on the Edge
14 HoursThis instructor-led, live training in Chile (online or onsite) is aimed at intermediate-level developers, data scientists, and tech enthusiasts who wish to gain practical skills in deploying AI models on edge devices for various applications.
By the end of this training, participants will be able to:
- Understand the principles of Edge AI and its benefits.
- Set up and configure the edge computing environment.
- Develop, train, and optimize AI models for edge deployment.
- Implement practical AI solutions on edge devices.
- Evaluate and improve the performance of edge-deployed models.
- Address ethical and security considerations in Edge AI applications.
Edge AI in Autonomous Systems
14 HoursThis instructor-led, live training in Chile (online or onsite) is aimed at intermediate-level robotics engineers, autonomous vehicle developers, and AI researchers who wish to leverage Edge AI for innovative autonomous system solutions.
By the end of this training, participants will be able to:
- Understand the role and benefits of Edge AI in autonomous systems.
- Develop and deploy AI models for real-time processing on edge devices.
- Implement Edge AI solutions in autonomous vehicles, drones, and robotics.
- Design and optimize control systems using Edge AI.
- Address ethical and regulatory considerations in autonomous AI applications.
Edge AI: From Concept to Implementation
14 HoursThis instructor-led, live training in Chile (online or onsite) is aimed at intermediate-level developers and IT professionals who wish to gain a comprehensive understanding of Edge AI from concept to practical implementation, including setup and deployment.
By the end of this training, participants will be able to:
- Understand the fundamental concepts of Edge AI.
- Set up and configure Edge AI environments.
- Develop, train, and optimize Edge AI models.
- Deploy and manage Edge AI applications.
- Integrate Edge AI with existing systems and workflows.
- Address ethical considerations and best practices in Edge AI implementation.
Edge AI for Financial Services
14 HoursThis instructor-led, live training in Chile (online or onsite) is aimed at intermediate-level finance professionals, fintech developers, and AI specialists who wish to implement Edge AI solutions in financial services.
By the end of this training, participants will be able to:
- Understand the role of Edge AI in financial services.
- Implement fraud detection systems using Edge AI.
- Enhance customer service through AI-driven solutions.
- Apply Edge AI for risk management and decision-making.
- Deploy and manage Edge AI solutions in financial environments.
Edge AI for Healthcare
14 HoursThis instructor-led, live training in Chile (online or onsite) is aimed at intermediate-level healthcare professionals, biomedical engineers, and AI developers who wish to leverage Edge AI for innovative healthcare solutions.
By the end of this training, participants will be able to:
- Understand the role and benefits of Edge AI in healthcare.
- Develop and deploy AI models on edge devices for healthcare applications.
- Implement Edge AI solutions in wearable devices and diagnostic tools.
- Design and deploy patient monitoring systems using Edge AI.
- Address ethical and regulatory considerations in healthcare AI applications.
Edge AI in Industrial Automation
14 HoursThis instructor-led, live training in Chile (online or onsite) is aimed at intermediate-level industrial engineers, manufacturing professionals, and AI developers who wish to implement Edge AI solutions in industrial automation.
By the end of this training, participants will be able to:
- Understand the role of Edge AI in industrial automation.
- Implement predictive maintenance solutions using Edge AI.
- Apply AI techniques for quality control in manufacturing processes.
- Optimize industrial processes using Edge AI.
- Deploy and manage Edge AI solutions in industrial environments.
Edge AI for IoT Applications
14 HoursThis instructor-led, live training in Chile (online or onsite) is aimed at intermediate-level developers, system architects, and industry professionals who wish to leverage Edge AI for enhancing IoT applications with intelligent data processing and analytics capabilities.
By the end of this training, participants will be able to:
- Understand the fundamentals of Edge AI and its application in IoT.
- Set up and configure Edge AI environments for IoT devices.
- Develop and deploy AI models on edge devices for IoT applications.
- Implement real-time data processing and decision-making in IoT systems.
- Integrate Edge AI with various IoT protocols and platforms.
- Address ethical considerations and best practices in Edge AI for IoT.
Deploying AI Models on Edge Devices with NVIDIA Jetson
21 HoursThis instructor-led, live training in Chile (online or onsite) is aimed at intermediate-level AI developers, embedded engineers, and robotics engineers who wish to optimize and deploy AI models on NVIDIA Jetson platforms for edge applications.
By the end of this training, participants will be able to:
- Understand the fundamentals of edge AI and NVIDIA Jetson hardware.
- Optimize AI models for deployment on edge devices.
- Use TensorRT for accelerating deep learning inference.
- Deploy AI models using JetPack SDK and ONNX Runtime.
Edge AI for Smart Cities
14 HoursThis instructor-led, live training in Chile (online or onsite) is aimed at intermediate-level urban planners, civil engineers, and smart city project managers who wish to leverage Edge AI for smart city initiatives.
By the end of this training, participants will be able to:
- Understand the role of Edge AI in smart city infrastructures.
- Implement Edge AI solutions for traffic management and surveillance.
- Optimize urban resources using Edge AI technologies.
- Integrate Edge AI with existing smart city systems.
- Address ethical and regulatory considerations in smart city deployments.
Edge AI with TensorFlow Lite
14 HoursThis instructor-led, live training in Chile (online or onsite) is aimed at intermediate-level developers, data scientists, and AI practitioners who wish to leverage TensorFlow Lite for Edge AI applications.
By the end of this training, participants will be able to:
- Understand the fundamentals of TensorFlow Lite and its role in Edge AI.
- Develop and optimize AI models using TensorFlow Lite.
- Deploy TensorFlow Lite models on various edge devices.
- Utilize tools and techniques for model conversion and optimization.
- Implement practical Edge AI applications using TensorFlow Lite.
Introduction to Edge AI
14 HoursThis instructor-led, live training in Chile (online or onsite) is aimed at beginner-level developers and IT professionals who wish to understand the fundamentals of Edge AI and its introductory applications.
By the end of this training, participants will be able to:
- Understand the basic concepts and architecture of Edge AI.
- Set up and configure Edge AI environments.
- Develop and deploy simple Edge AI applications.
- Identify and understand the use cases and benefits of Edge AI.
Low-Power AI: Optimizing Edge AI for Energy-Efficient Devices
21 HoursThis instructor-led, live training in Chile (online or onsite) is aimed at advanced-level AI engineers, embedded developers, and hardware engineers who wish to implement AI models on low-power devices while minimizing energy consumption.
By the end of this training, participants will be able to:
- Understand the challenges of running AI on energy-efficient devices.
- Optimize neural networks for low-power inference.
- Utilize quantization, pruning, and model compression techniques.
- Deploy AI models on edge hardware with minimal power usage.
Optimizing AI Models for Edge Devices
14 HoursThis instructor-led, live training in Chile (online or onsite) is aimed at intermediate-level AI developers, machine learning engineers, and system architects who wish to optimize AI models for edge deployment.
By the end of this training, participants will be able to:
- Understand the challenges and requirements of deploying AI models on edge devices.
- Apply model compression techniques to reduce the size and complexity of AI models.
- Utilize quantization methods to enhance model efficiency on edge hardware.
- Implement pruning and other optimization techniques to improve model performance.
- Deploy optimized AI models on various edge devices.
Security and Privacy in Edge AI
14 HoursThis instructor-led, live training in Chile (online or onsite) is aimed at intermediate-level cybersecurity professionals, system administrators, and AI ethics researchers who wish to secure and ethically deploy Edge AI solutions.
By the end of this training, participants will be able to:
- Understand the security and privacy challenges in Edge AI.
- Implement best practices for securing edge devices and data.
- Develop strategies to mitigate security risks in Edge AI deployments.
- Address ethical considerations and ensure compliance with regulations.
- Conduct security assessments and audits for Edge AI applications.