Edge AI Training in Gaia

This instructor-led, live training in Gaia (online or on-site) is designed for advanced-level AI researchers, data scientists, and security specialists seeking to implement federated learning techniques to train AI models across multiple edge devices while preserving data privacy.

Upon completing this training, participants will be able to:

• Grasp the principles and advantages of federated learning within Edge AI.
• Develop federated learning models using TensorFlow Federated and PyTorch.
• Enhance AI training performance across distributed edge devices.
• Tackle data privacy and security challenges inherent in federated learning.
• Deploy and oversee federated learning systems in practical applications.

This instructor-led live training in Gaia (online or onsite) is aimed at beginner to intermediate 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 Gaia (online or onsite) is designed for advanced-level cybersecurity professionals, AI engineers, and IoT developers who aim to implement robust security measures and resilience strategies for Edge AI systems.

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

• Identify security risks and vulnerabilities in Edge AI deployments.
• Deploy encryption and authentication techniques to protect data.
• 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 Gaia (online or onsite) is designed for beginner to intermediate-level retail technologists, AI developers, and business analysts seeking 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:

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

This instructor-led, live training in Gaia (online or onsite) targets intermediate-level telecom professionals, AI engineers, and IoT specialists keen to explore how 5G networks accelerate Edge AI applications.

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

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

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

Upon completing this training, participants will be capable of:

• Grasping the fundamentals of Edge AI and its role in computer vision.
• Deploying optimized deep learning models on edge devices for real-time image and video analysis.
• Utilizing frameworks such as TensorFlow Lite, OpenVINO, and NVIDIA Jetson SDK for model deployment.
• Optimizing AI models to achieve superior performance, power efficiency, and low-latency inference.

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

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

• Comprehending the core principles of TinyML and edge AI.
• Deploying lightweight AI models onto microcontrollers.
• Optimizing AI inference to minimize power usage.
• Integrating TinyML into practical IoT applications.

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

Upon completion 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 hands-on course in Gaia guides you through the process of deploying agentic AI on resource-constrained devices. You will learn to construct, optimize, and manage lightweight agents for local reasoning using Python, TensorFlow Lite, and PyTorch Mobile, ultimately enhancing speed, privacy, and reliability.

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

Upon completing 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.
• Utilize TensorRT to accelerate deep learning inference.
• Deploy AI models using JetPack SDK and ONNX Runtime.

This instructor-led, live training in Gaia (online or onsite) is designed for intermediate-level AI developers, machine learning engineers, and system architects who wish to optimize AI models for edge deployment.

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

• Grasping the challenges and prerequisites associated with deploying AI models on edge devices.
• Applying model compression strategies to decrease the size and complexity of AI models.
• Leveraging quantization approaches to boost model efficiency on edge hardware.
• Executing pruning and other optimization techniques to enhance model performance.
• Deploying refined AI models across various edge devices.

This live, instructor-led training in Gaia (available online or onsite) targets 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 end of the program, participants will be able to:

• Understand the fundamentals of Edge AI and its key 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.

This instructor-led, live training in Gaia (online or onsite) is designed for intermediate-level finance professionals, fintech developers, and AI specialists aiming to implement Edge AI solutions in financial services.

Upon completing this training, participants will be able to:

• Comprehend the role of Edge AI in financial services.
• Deploy fraud detection systems using Edge AI.
• Improve customer service through AI-driven solutions.
• Apply Edge AI for risk management and decision-making.
• Deploy and manage Edge AI solutions within financial environments.

This instructor-led, live training in Gaia (online or onsite) is designed for 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:

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

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

• Grasp 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 Gaia (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 Gaia (online or onsite) is designed for intermediate-level cybersecurity professionals, system administrators, and AI ethics researchers keen to secure and ethically deploy Edge AI solutions.

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

• Grasp the security and privacy challenges inherent in Edge AI.
• Apply best practices for protecting edge devices and data.
• Formulate strategies to mitigate security risks in Edge AI deployments.
• Navigate ethical considerations and ensure adherence to regulations.
• Perform security assessments and audits for Edge AI applications.

This instructor-led, live training in Gaia (online or onsite) is designed for intermediate-level robotics engineers, autonomous vehicle developers, and AI researchers seeking 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.
• 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 Gaia (online or onsite) is designed for healthcare professionals, biomedical engineers, and AI developers at an intermediate level who wish to utilise Edge AI for innovative healthcare solutions.

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

• Comprehend the role and advantages of Edge AI in healthcare.
• Create and deploy AI models on edge devices for healthcare applications.
• Implement Edge AI solutions within wearable devices and diagnostic tools.
• Design and deploy patient monitoring systems using Edge AI.
• Tackle ethical and regulatory issues pertaining to healthcare AI applications.

This live training in Gaia equips intermediate engineers with the skills to deploy TinyML models for robotics. Participants will learn to optimize inference for speed and energy efficiency, integrate AI into control systems, and develop autonomous, low-latency robotic solutions directly on embedded hardware.

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

This instructor-led, live training in Gaia (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 Gaia (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 Gaia (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:

• Grasp the fundamentals of TinyML and its applications in IoT.
• Configure a TinyML development environment for IoT projects.
• Create 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 Gaia (online or onsite) is designed for intermediate-level developers and IT professionals who wish to gain a thorough understanding of Edge AI, covering everything 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 adhere to best practices in Edge AI implementation.

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

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

• Understand the fundamentals of TinyML and its benefits for edge AI applications.
• Set up a development environment for TinyML projects.
• Train, optimize, and deploy AI models on low-power microcontrollers.
• Use TensorFlow Lite and Edge Impulse to implement real-world TinyML applications.
• Optimize AI models for power efficiency and memory constraints.

This instructor-led, live training in Gaia (online or onsite) is designed for beginner-level developers and IT professionals who want to grasp the fundamentals of Edge AI and its initial applications.

Upon completing this training, participants will be able to:

• Grasp the core concepts and architecture of Edge AI.
• Establish and configure Edge AI environments.
• Create and deploy simple Edge AI applications.
• Recognize and comprehend the use cases and advantages of Edge AI.