Edge AI Course Online Certification

10 Lesson

40 Hours

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igmGuru offers the best Edge AI Training online worldwide. Our course covers essential modules such as Edge Device Architecture, On-Device Machine Learning, Model Optimization Techniques, Real-Time Inference, Edge Deployment Strategies, Federated Learning, and Edge AI Security. This course syllabus is crafted by industry experts with over 15 years of experience in AI, IoT, and embedded systems, featuring practical and hands-on projects tailored for real-world applications. We have successfully trained 350+ professionals globally. Enroll in our Edge AI course to prepare for industry-recognized Edge AI certifications and become a certified Edge AI specialist.

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Overview

Prerequisites

Basic understanding of AI and Machine Learning
Proficiency in Python programming
Familiarity with embedded systems or IoT devices
Working knowledge of Linux and command-line tools

What Will You Learn

Fundamentals of Edge Artificial Intelligence
Edge computing architecture and real-time data processing
Model optimization with quantization, pruning, and distillation
AI model deployment using TensorFlow Lite, ONNX Runtime, and OpenVINO
TinyML development on microcontrollers
Edge–Cloud collaboration and hybrid inference
Federated learning and decentralized AI training
Generative AI deployment on edge devices
Edge MLOps and lifecycle management
Security, privacy, and energy-efficient AI design

Key Features

1 On 1 Training Option Available
24 X 7 Lifetime Support & Access
Small Batches Upto 10 Participants
Experienced & Professional Trainers
Flexible Schedule
Instructor Led Training : 40 Hrs

Course Curriculum

Lesson 1 - Foundations of Edge AI

1. Overview of Edge AI vs Cloud AI.

2. Edge hardware taxonomy: microcontrollers, SBCs, GPUs, NPUs, and FPGAs.

3. Use-cases: smart cameras, wearables, autonomous systems, industrial IoT.

4. Edge deployment frameworks and toolchains overview.

Lesson 2 - Data and Model Fundamentals

1. Dataset preparation for edge deployment.

2. Neural network architectures suited for constrained devices (MobileNet, EfficientNet, TinyYOLO).

3. Metrics: latency, throughput, power, accuracy trade-offs.

Lesson 3 - Model Optimization and Compression

1. Quantization, pruning, clustering, and knowledge distillation.

2. Hardware-aware NAS (Neural Architecture Search).

3. Tools: TensorFlow Model Optimization Toolkit, OpenVINO Model Optimizer, ONNX Runtime Mobile.

Lesson 4 - TinyML and Ultra-Low-Power AI

1. Running AI models on MCUs with kilobytes of memory.

2. TensorFlow Lite for Microcontrollers, STM32Cube.AI, and Edge Impulse workflows.

3. Power optimization and deployment considerations.

Lesson 5 - Edge Hardware & Deployment Architectures

1. Edge devices deep dive: NVIDIA Jetson, Coral TPU, Raspberry Pi, Renesas RA.

2. Accelerators and NPUs for AI inference.

3. End-to-end deployment pipeline: training → conversion → deployment → inference.

Lesson 6 - Edge-Cloud Collaboration

1. Split computing and offloading strategies.

2. Real-time inference with hybrid AI systems.

3. Streaming and event-based pipelines (MQTT, Kafka, AWS Greengrass).

Lesson 7 - Federated Learning on the Edge

1. Concept of decentralized model training.

2. Data privacy and differential privacy techniques.

3. Tools: TensorFlow Federated, PySyft, Flower.

Lesson 8 - Generative AI and Foundation Models at the Edge

1. Compact generative architectures (Tiny-Diffusion, MobileLLMs).

2. Running LLMs on edge devices (e.g., Llama Edge, NanoGPT).

3. Use-cases: speech generation, real-time vision enhancement, chatbots in embedded environments.

Lesson 9 - Edge MLOps and Lifecycle Management

1. Monitoring and updating deployed models.

2. Model versioning, A/B testing, rollback strategies.

3. CI/CD pipelines for edge devices; containerization (Docker, K3s).

Lesson 10 - Security, Privacy, and Energy-Aware AI

1. Secure model deployment, encrypted inference, threat surfaces.

2. Privacy-preserving techniques: differential privacy, homomorphic encryption.

3. Energy-aware and sustainable AI: balancing performance with power efficiency.

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Course Fees

Online Class Room Program

US $ 799.00

100% Money Back Guarantee

Duration : 40 Hrs
Plus Self Paced

Classes Starting From

Fast Track Batch 16 Jun 2026
Weekday Batch 22 Jun 2026
Weekend Batch 20 Jun 2026

Corporate Training

Customized Training Delivery Model
Flexible Training Schedule Options
Industry Experienced Trainers
24x7 Support

Trusted By Top Companies Worldwide

Want to know Today's Offer

Intel Edge AI Certification

Official certification exam name is Intel Edge AI Certification – Edge AI for IoT Developers.

Exam Format

Duration: 120 minutes (2 hours)
Number of Questions: 60 multiple-choice and scenario-based questions
Passing Score: Around 70%
Type: Knowledge-based and practical application questions
Mode: Online proctored or at an authorized Intel test center

Exam Cost

Typically ranges from $200 to $400 USD, depending on location and delivery partner.

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Edge AI Course Online Certification