Edge AI Training in Lyon

This instructor-led, live training in Lyon (online or onsite) targets advanced AI researchers, data scientists, and security professionals who aim to implement federated learning techniques for training AI models across multiple edge devices while maintaining data privacy.

By the end of this training, participants will be able to:

• Understand the principles and benefits of federated learning in Edge AI.
• Implement federated learning models using TensorFlow Federated and PyTorch.
• Optimize AI training across distributed edge devices.
• Address data privacy and security challenges in federated learning.
• Deploy and monitor federated learning systems in real-world applications.

This instructor-led, live training in Lyon (available online or on-site) is aimed at beginner to intermediate-level agritech professionals, IoT specialists, and AI engineers who wish to develop and deploy Edge AI solutions for smart farming.

By the end of this training, participants will be able to:

• Understand the role of Edge AI in precision agriculture.
• Implement AI-driven crop and livestock monitoring systems.
• Develop automated irrigation and environmental sensing solutions.
• Optimize agricultural efficiency using real-time Edge AI analytics.

This instructor-led, live training in Lyon (online or onsite) targets advanced cybersecurity professionals, AI engineers, and IoT developers seeking to implement robust security measures and resilience strategies for Edge AI systems.

By the end of this training, participants will be able to:

• Identify security risks and vulnerabilities associated with Edge AI deployments.
• Deploy encryption and authentication methods to ensure data protection.
• Design resilient Edge AI architectures capable of withstanding cyber threats.
• Apply secure AI model deployment strategies within edge environments.

This instructor-led, live training in Lyon (online or on-site) is designed for beginner to intermediate retail technologists, AI developers, and business analysts who wish to apply Edge AI solutions for smart checkout systems, inventory management, and personalized customer engagement.

Upon completion of this training, participants will be able to:

• Comprehend how Edge AI enhances retail operations and customer experience.
• Deploy AI-driven smart checkout and cashier-less payment systems.
• Optimize inventory management through real-time tracking and analytics.
• Leverage computer vision and AI to create personalized in-store experiences.

This instructor-led, live training in Lyon (online or onsite) is designed for intermediate-level telecom professionals, AI engineers, and IoT specialists seeking to understand how 5G networks accelerate Edge AI applications.

Upon completion of this training, participants will be able to:

• Grasp the fundamentals of 5G technology and its influence on Edge AI.
• Deploy AI models optimized for low-latency applications within 5G environments.
• Implement real-time decision-making systems leveraging Edge AI and 5G connectivity.
• Optimize AI workloads to ensure efficient performance on edge devices.

This instructor-led, live training in Lyon (online or onsite) is designed for intermediate-level embedded AI developers and edge computing specialists who wish to fine-tune and optimize lightweight AI models for deployment on resource-constrained devices.

By the end of this training, participants will be able to:

• Select and adapt pre-trained models suitable for edge deployment.
• Apply quantization, pruning, and other compression techniques to reduce model size and latency.
• Fine-tune models using transfer learning for task-specific performance.
• Deploy optimized models on real edge hardware platforms.

This instructor-led, live training in Lyon (online or onsite) is designed for intermediate to advanced computer vision engineers, AI developers, and IoT professionals who want to implement and optimize computer vision models for real-time processing on edge devices.

Upon completing this training, participants will be able to:

• Grasp the fundamentals of Edge AI and its applications in computer vision.
• Deploy optimized deep learning models on edge devices for real-time image and video analysis.
• Utilize frameworks such as TensorFlow Lite, OpenVINO, and NVIDIA Jetson SDK for model deployment.
• Optimize AI models for performance, power efficiency, and low-latency inference.

This instructor-led, live training in Lyon (online or onsite) is aimed at intermediate-level embedded engineers, IoT developers, and AI researchers who wish to implement TinyML techniques for AI-powered applications on energy-efficient hardware.

By the end of this training, participants will be able to:

• Understand the fundamentals of TinyML and edge AI.
• Deploy lightweight AI models on microcontrollers.
• Optimize AI inference for low-power consumption.
• Integrate TinyML with real-world IoT applications.

This instructor-led, live training in Lyon (online or onsite) is designed for intermediate to advanced robotics engineers, AI developers, and automation specialists aiming to implement Edge AI for robotic applications.

By the end of this training, participants will be able to:

• Comprehend the significance of Edge AI in autonomous systems.
• Deploy AI models on edge devices to enable real-time robotics operations.
• Enhance AI performance to ensure low-latency decision-making.
• Combine computer vision with sensor fusion techniques to achieve robotic autonomy.

This instructor-led, live training in Lyon (online or onsite) is designed for advanced AI engineers, embedded developers, and hardware engineers who aim to deploy AI models on low-power devices while significantly reducing energy consumption.

Upon completion of this training, participants will be able to:

• Grasp the challenges associated with running AI on energy-efficient devices.
• Optimize neural networks for low-power inference tasks.
• Apply quantization, pruning, and model compression techniques.
• Deploy AI models on edge hardware with minimal power usage.

This instructor-led, live training in Lyon (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.

This instructor-led, live training in Lyon (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.

This instructor-led live training, conducted in Lyon (online or onsite), targets intermediate-level developers, data scientists, and technology enthusiasts aiming to build practical skills in deploying AI models on edge devices for diverse applications.

By the end of this training, participants will be able to:

• Understand the core principles of Edge AI and its associated benefits.
• Establish and configure the necessary edge computing environment.
• Create, train, and optimize AI models for deployment on edge systems.
• Implement practical AI solutions using edge hardware.
• Assess and improve the performance of models running on edge devices.
• Navigate ethical and security considerations inherent in Edge AI applications.

This instructor-led, live training in Lyon (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.

This instructor-led, live training in Lyon (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.

This instructor-led, live training in Lyon (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.

This instructor-led, live training in Lyon (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.

This instructor-led, live training in Lyon (online or onsite) is designed for intermediate-level cybersecurity professionals, system administrators, and AI ethics researchers who aim to secure and ethically deploy Edge AI solutions.

By the conclusion 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.

This instructor-led, live training in Lyon (online or onsite) is designed for intermediate-level robotics engineers, autonomous vehicle developers, and AI researchers who wish to leverage Edge AI for innovative autonomous system solutions.

Upon completion of this training, participants will be able to:

• Grasp the role and advantages of Edge AI in autonomous systems.
• Create 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 utilizing Edge AI.
• Address ethical and regulatory considerations in autonomous AI applications.

This live, instructor-led training held in Lyon (online or onsite) is designed for intermediate-level healthcare professionals, biomedical engineers, and AI developers seeking to harness 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.

This instructor-led, live training in Lyon (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.

This instructor-led live training, conducted in Lyon (online or onsite), is designed for intermediate developers, system architects, and industry professionals aiming to utilize Edge AI to enhance IoT applications with intelligent data processing and analytics.

By the conclusion of this training, participants will be able to:

• Comprehend 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.

This instructor-led, live training in Lyon (online or onsite) is designed for intermediate-level IoT developers, embedded engineers, and AI practitioners who want to apply TinyML for predictive maintenance, anomaly detection, and smart sensor solutions.

Upon completing this training, participants will be able to:

• Grasp the core principles of TinyML and its role in IoT ecosystems.
• Configure a TinyML development environment tailored for IoT projects.
• Create and deploy machine learning models on low-power microcontrollers.
• Apply TinyML techniques for predictive maintenance and anomaly detection.
• Refine TinyML models to maximize power efficiency and minimize memory consumption.

This instructor-led live training in Lyon (available online or onsite) targets intermediate-level developers and IT professionals who wish to gain a comprehensive understanding of Edge AI, from conceptual foundations 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.

This instructor-led, live training in Lyon (online or onsite) is designed for intermediate-level embedded systems engineers and AI developers who want to deploy machine learning models on microcontrollers using TensorFlow Lite and Edge Impulse.

Upon completion of this training, participants will be able to:

• Grasp the fundamentals of TinyML and its advantages for edge AI applications.
• Configure a development environment tailored for TinyML projects.
• Train, optimize, and deploy AI models on low-power microcontrollers.
• Utilize TensorFlow Lite and Edge Impulse to build real-world TinyML solutions.
• Optimize AI models to meet power efficiency and memory limitations.

This instructor-led, live training in Lyon (online or onsite) targets beginner-level developers and IT professionals seeking to understand the fundamentals of Edge AI and its introductory applications.

By the end of this training, participants will be able to:

• Grasp 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.