Edge AI: Deploying AI on Edge Devices Course

Introduction to the Course

The course on Edge AI will teach you how to create and deliver AI solutions right on the edge (smartphones, IoT devices, Embedded Systems, Drones and Industrial Controllers). As more businesses deploy their AI solutions in addition to cloud-based only solutions, there are benefits around reducing latency, improving privacy, reducing bandwidth costs and gaining real time analytics on data as close to where the IoT devices are as possible. In this course you will learn how to optimize your AI models for deployment at the edge, use hardware acceleration, create pipelines to deliver applications efficiently, and the last topic will focus on real world applications built at the edge.

Course Objectives

• Understand the fundamentals of Edge AI and its role in modern computing.

• Learn how AI models are adapted for deployment on edge devices with limited resources.

• Gain practical skills using edge platforms, tools, and frameworks for deployment.

• Learn techniques for optimizing AI models for speed, memory, and power consumption.

• Build, deploy, and evaluate AI systems running locally on edge devices.

What Will You Learn (Modules)

Module 1: Introduction to Edge AI

• Overview of Edge AI: Concepts and Applications
• Benefits of AI at the Edge vs. Cloud AI
• Use Cases in Smart Cities, Healthcare, Autonomous Systems, and IoT

Module 2: Edge AI Architectures and Devices

• Edge Devices Overview: Raspberry Pi, NVIDIA Jetson, Google Coral, Qualcomm AI Engine
• System Architectures for Edge AI
• Hardware Constraints: Memory, Power, and Processing Limits

Module 3: Building and Optimizing AI Models for Edge Devices

• Model Compression Techniques: Pruning, Quantization
• Knowledge Distillation for Lightweight Models
• Frameworks for Edge AI Development: TensorFlow Lite, PyTorch Mobile, OpenVINO

Module 4: Convolutional Neural Networks (CNNs) for Edge AI

• Lightweight CNN Architectures (MobileNet, SqueezeNet, EfficientNet)
• Real-Time Image Processing on Edge Devices
• Optimizing CNNs for Mobile and IoT Applications

Module 5: Recurrent Neural Networks (RNNs) and NLP on Edge Devices

• Deploying NLP Models (BERT, GPT) on Edge Devices
• Speech Recognition and Processing on Edge
• Applications in Real-Time Language Translation and Assistants

Module 6: Real-Time Inference and Streaming on Edge

• Real-Time Video and Image Analytics on Edge
• Object Detection and Tracking with Edge Devices
• Streaming Data Processing with AI at the Edge

Module 7: Energy Efficiency and Power Management for Edge AI

• Power-Efficient AI Inference
• Managing Resource Constraints (Battery, CPU, GPU)
• Low-Power AI Hardware for Edge Devices

Who should take this course

This course is ideal for:

• AI Engineers & Developers: Who want to deploy AI beyond the cloud.

• Embedded Systems Engineers: Who want to add intelligence to hardware.

• Data Scientists: Who wish to optimize models for real-time, low-power environments.

• IoT Professionals: Who want to build smarter IoT solutions.

• Students & Enthusiasts: Who want hands-on experience with edge computing and AI.

Job Opportunities

• Edge AI Engineer: Building AI solutions for edge devices.

• Embedded AI Developer: Implementing models on hardware platforms.

• IoT AI Specialist: Integrating AI into IoT systems.

• Computer Vision Engineer (Edge): Deploying vision models on devices.

• AI Systems Architect: Designing cloud-edge hybrid architectures.

Why Learn with Nano School

At Nanoschool, you will receive expert-led training in Edge AI with practical, hands-on experience. Key benefits include:

• Expert Instructors: Learn from professionals experienced in AI and embedded systems.

• Hands-On Learning: Work on real edge devices and real-world datasets.

• Industry-Relevant Curriculum: Stay updated with the latest trends in edge computing.

• Career Support: Get career guidance and job placement support.

 Key Outcomes of the Course

After completing the course, you will be able to:

• Build and deploy optimized AI models on edge devices.

• Select appropriate hardware and tools for edge AI applications.

• Implement real-time inference and local analytics.

• Manage and update edge AI systems securely.

• Build edge AI solutions for industries such as retail, manufacturing, healthcare, and smart cities.

Enroll now and discover how AI can be deployed at the edge. Learn to build intelligent systems that operate faster, safer, and smarter.