Edge AI Fundamentals Training is a comprehensive two-day program designed to introduce learners to the principles of deploying artificial intelligence at the edge, where data is processed locally on devices rather than in centralized cloud environments. This course focuses on Edge AI concepts, architectures, hardware considerations, model optimization, and real-world use cases, enabling participants to understand how low-latency, secure, and efficient AI systems operate across IoT, industrial, and smart device environments.

• Understand Edge AI fundamentals
• Learn edge computing concepts
• Understand real-time AI processing
• Gain knowledge of AI deployment at the edge
• Learn IoT and edge integration basics
• Understand low-latency AI systems
• Explore edge AI use cases
• Identify edge computing challenges

• AI and machine learning professionals
• IoT and embedded systems engineers
• Edge computing and infrastructure specialists
• Robotics and automation professionals
• Technology leaders exploring decentralized AI

• Basic understanding of artificial intelligence or machine learning concepts
• Familiarity with IoT devices, embedded systems, or edge computing concepts
• General awareness of data processing and analytics
• Interest in real-time and distributed AI systems

• Understanding core concepts of Edge AI and edge computing
• Awareness of deploying AI models on edge devices
• Knowledge of model optimization for low-resource environments
• Evaluating latency, security, and performance trade-offs
• Interpreting real-world Edge AI applications

By the end of this training, participants will be able to explain Edge AI fundamentals, understand edge architectures and hardware constraints, evaluate model deployment and optimization strategies, and assess real-world applications and future trends of AI at the edge.

Introduction to Edge AI
• Definition and scope of Edge AI
• Difference between cloud AI and Edge AI
• Benefits of edge intelligence such as low latency and privacy
• Overview of Edge AI use cases

Edge AI Architecture and Hardware
• Edge devices, sensors, and gateways
• CPUs, GPUs, NPUs, and AI accelerators
• Data flow between edge, fog, and cloud
• Hardware constraints and design considerations

AI Models for Edge Deployment
• Selecting models suitable for edge environments
• Model size, latency, and accuracy trade-offs
• Lightweight neural networks and architectures
• Evaluating performance on edge devices

Model Optimization and Deployment
• Model compression and quantization concepts
• Pruning and efficiency techniques
• Deployment pipelines for edge AI
• Updating and maintaining models at the edge

Security, Privacy, and Reliability
• Data privacy and on-device processing
• Securing edge AI systems
• Reliability and fault tolerance
• Managing risks in distributed AI deployments

Edge AI Use Cases and Future Trends
• Smart cameras and computer vision at the edge
• Industrial IoT and predictive maintenance
• Autonomous devices and robotics
• Future directions of Edge AI and intelligent edge systems

• Edge AI concepts
• Edge computing fundamentals
• Real-time AI processing
• IoT and edge integration
• AI model deployment basics
• Edge devices and architectures
• Low-latency AI systems
• Industrial edge AI applications
• Security and privacy concepts
• Practical edge AI scenarios

• Conceptual understanding assessments
• Edge AI use case analysis exercises
• Model deployment and optimization discussion
• Final knowledge evaluation quiz

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Participants who successfully complete the training will receive an AcadNXT Certification in Edge AI Fundamentals Training, validating their expertise in understanding Edge AI concepts, architectures, deployment considerations, and real-world applications.