Online/ e-LMS
Mentor Based
Moderate
3 Weeks
About
Edge AI refers to running AI models directly on edge devices such as smartphones, sensors, and IoT devices, enabling faster inferences and reducing data transmission to the cloud. This course covers AI deployment on hardware-constrained devices, model optimization techniques, and tools like TensorFlow Lite and ONNX.
Aim
This program aims to teach professionals and researchers the practical skills needed to deploy AI models on edge devices, enabling real-time processing and decision-making without reliance on cloud infrastructure.
Program Objectives
- Understand the fundamentals of Edge AI and its applications.
- Learn techniques for optimizing AI models for deployment on edge devices.
- Gain proficiency in tools like TensorFlow Lite, ONNX, and PyTorch Mobile.
- Develop and deploy AI solutions for real-time processing on edge devices.
- Address security and privacy challenges in edge computing environments.
Program Structure
- 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
- 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
- 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
- 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
- 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
- Model Deployment on Edge Devices
- Deploying AI Models with TensorFlow Lite and ONNX Runtime
- Edge AI Deployment with NVIDIA Jetson and OpenVINO
- Real-World Application Deployment on Raspberry Pi and Mobile Devices
- 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
- Edge AI for IoT and Smart Devices
- Integration of AI with IoT Networks
- Use Cases in Smart Homes, Wearables, and Industry 4.0
- Deploying AI in Low-Resource IoT Environments
- Security and Privacy in Edge AI
- Privacy Concerns with AI at the Edge
- Secure Deployment and Data Encryption
- Federated Learning for Privacy-Preserving AI
- 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
- Edge AI in Autonomous Systems
- AI for Drones, Self-Driving Cars, and Robots
- Real-Time Decision-Making in Autonomous Systems
- Challenges and Case Studies in Edge AI Deployment
Participant’s Eligibility
Data scientists, AI engineers, embedded systems developers, and researchers focused on deploying AI models on hardware-constrained devices.
Program Outcomes
- Ability to deploy and manage AI models on edge devices.
- Proficiency in optimizing models for memory and processing constraints.
- Real-time decision-making capabilities using AI on hardware-constrained environments.
- Knowledge of privacy, security, and performance trade-offs in edge AI.
Fee Structure
Fee: INR 8,499 USD 112
We are excited to announce that we now accept payments in over 20 global currencies, in addition to USD. Check out our list to see if your preferred currency is supported. Enjoy the convenience and flexibility of paying in your local currency!
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Key Takeaways
Program Deliverables
- Access to e-LMS
- Real Time Project for Dissertation
- Project Guidance
- Paper Publication Opportunity
- Self Assessment
- Final Examination
- e-Certification
- e-Marksheet
Future Career Prospects
- Edge AI Engineer
- Embedded AI Developer
- IoT AI Specialist
- AI Systems Architect
- AI Solution Engineer for Smart Devices
- AI Innovation Specialist for Edge Computing
Job Opportunities
- Edge AI Engineer
- Embedded AI Developer
- IoT AI Specialist
- AI Systems Architect
- AI Solution Engineer for Smart Devices
- AI Innovation Specialist for Edge Computing
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