Edge AI Training in Leon

This instructor-led, live training in Leon (online or onsite) is aimed at advanced-level AI researchers, data scientists, and security specialists who wish to implement federated learning techniques for training AI models across multiple edge devices while preserving 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 Leon (online or onsite) is designed for beginner to intermediate agritech professionals, IoT specialists, and AI engineers who want 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 Leon (online or onsite) is aimed at advanced-level cybersecurity professionals, AI engineers, and IoT developers who wish to implement robust security measures and resilience strategies for Edge AI systems.

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

• Understand security risks and vulnerabilities in Edge AI deployments.
• Implement encryption and authentication techniques for data protection.
• Design resilient Edge AI architectures that can withstand cyber threats.
• Apply secure AI model deployment strategies in edge environments.

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

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

• Understand how Edge AI enhances retail operations and customer experience.
• Implement AI-powered smart checkout and cashier-less payment systems.
• Optimize inventory management with real-time tracking and analytics.
• Utilize computer vision and AI for personalized in-store experiences.

This instructor-led, live training in Leon (online or onsite) is designed for intermediate-level telecom professionals, AI engineers, and IoT specialists interested in exploring 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 achieve efficient performance on edge devices.

This instructor-led, live training in Leon (online or onsite) is designed for intermediate-level embedded AI developers and edge computing specialists aiming to fine-tune and optimize lightweight AI models for deployment on devices with limited resources.

Upon completion of this training, participants will be capable of:

• Selecting and adapting pre-trained models that are appropriate for edge deployment.
• Implementing quantization, pruning, and other compression techniques to decrease model size and reduce latency.
• Fine-tuning models through transfer learning to achieve task-specific performance enhancements.
• Deploying optimized models onto actual edge hardware platforms.

This instructor-led, live training in Leon (online or onsite) is designed for computer vision engineers, AI developers, and IoT professionals at intermediate to advanced levels 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 Leon (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 live, instructor-led training in Leon (offered online or on-site) targets robotics engineers, AI developers, and automation specialists at intermediate to advanced levels who wish to implement Edge AI for robotic applications.

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

• Comprehend 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.

This practical course in Leon walks you through the process of deploying agentic AI on resource-limited devices. Gain the expertise to build, optimize, and manage lightweight agents for local reasoning using Python, TensorFlow Lite, and PyTorch Mobile, ultimately boosting speed, privacy, and reliability.

This instructor-led, live training in Leon (online or on-site) 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:

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

This instructor-led, live training in Leon (online or onsite) is designed for intermediate-level AI developers, embedded engineers, and robotics engineers seeking to optimize and deploy AI models on NVIDIA Jetson platforms for edge applications.

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

• Grasp the fundamentals of edge AI and NVIDIA Jetson hardware.
• Optimize AI models for deployment on edge devices.
• Leverage TensorRT to accelerate deep learning inference.
• Deploy AI models using JetPack SDK and ONNX Runtime.

This instructor-led, live training in Leon (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 live, instructor-led training in Leon (available online or on-site) is tailored for intermediate-level developers, data scientists, and tech enthusiasts aiming to acquire practical skills in deploying AI models on edge devices for diverse applications.

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

• Comprehend the core principles of Edge AI and its associated benefits.
• Establish and configure the edge computing environment.
• Create, train, and optimize AI models for edge deployment.
• Execute practical AI solutions on edge hardware.
• Evaluate and refine the performance of edge-deployed models.
• Address ethical and security factors in Edge AI applications.

This instructor-led, live training in Leon (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 Leon (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.

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

• Grasp 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 Leon (online or onsite) targets intermediate-level developers, data scientists, and AI practitioners seeking to leverage TensorFlow Lite for Edge AI applications.

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

• Comprehend 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 Leon (online or in-person) is designed for intermediate-level urban planners, civil engineers, and smart city project managers aiming to leverage Edge AI for urban development projects.

By the conclusion 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 Leon (online or onsite) targets intermediate-level cybersecurity professionals, system administrators, and AI ethics researchers who wish to secure and ethically deploy Edge AI solutions.

Upon completing this training, participants will be able to:

• Grasp the security and privacy challenges inherent in Edge AI.
• Apply best practices for safeguarding edge devices and data.
• Create strategies to mitigate security risks in Edge AI deployments.
• Address ethical concerns and ensure regulatory compliance.
• Perform security assessments and audits for Edge AI applications.

This instructor-led, live training in Leon (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.

This instructor-led, live training in Leon (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.

This live training in Leon guides intermediate engineers in deploying TinyML models for robotics. It covers optimizing inference for speed and energy, integrating AI into control systems, and developing autonomous, low-latency robotic solutions directly on embedded hardware.

This 21-hour training in Leon empowers IT architects to design next-generation distributed systems. It covers the integration of 6G, edge computing, and AI to build low-latency, scalable infrastructures. Participants will develop practical skills to create secure, resilient, and intelligent edge architectures that meet future business needs.

This instructor-led, live training in Leon (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 in Leon (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.

This instructor-led, live training in Leon (online or onsite) is aimed at intermediate-level IoT developers, embedded engineers, and AI practitioners who wish to implement TinyML for predictive maintenance, anomaly detection, and smart sensor applications.

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

• Understand the fundamentals of TinyML and its applications in IoT.
• Set up a TinyML development environment for IoT projects.
• Develop and deploy ML models on low-power microcontrollers.
• Implement predictive maintenance and anomaly detection using TinyML.
• Optimize TinyML models for efficient power and memory usage.

This instructor-led, live training in Leon (online or on-site) is designed for intermediate-level developers and IT professionals seeking a thorough understanding of Edge AI, from conceptual foundations to practical implementation, including setup and deployment.

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

• Grasp 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 Leon (online or onsite) targets intermediate-level embedded systems engineers and AI developers seeking to deploy machine learning models on microcontrollers using TensorFlow Lite and Edge Impulse.

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

• Understand the core principles of TinyML and its benefits for edge AI applications.
• Establish a development environment for TinyML initiatives.
• Train, optimize, and deploy AI models on low-power microcontrollers.
• Use TensorFlow Lite and Edge Impulse to create real-world TinyML applications.
• Optimize AI models for power efficiency and memory limitations.

This instructor-led, live training session in Leon (online or in-person) is designed for beginner-level developers and IT professionals seeking to understand the fundamentals of Edge AI and its introductory applications.

Upon completing this training, participants will be able to:

• Comprehend the core concepts and architecture of Edge AI.
• Configure and establish Edge AI environments.
• Create and deploy basic Edge AI applications.
• Recognize and evaluate the practical use cases and advantages of Edge AI.