• Understand the fundamentals of Edge AI and TinyML.

• Learn edge hardware architectures and compute constraints.

• Implement model quantization and pruning techniques.

• Build lightweight deep-learning models for IoT devices.

• Use frameworks like TensorFlow Lite and PyTorch Mobile.

• Deploy neural networks on microcontrollers and edge boards.

• Optimize inference performance and energy efficiency.

• Integrate Edge AI with sensor data and IoT applications.

• Monitor and update deployed models remotely.

• Prepare for Edge AI developer or embedded-AI engineering roles.

Course Syllabus

Module 1: Introduction to Edge AI and TinyML Concepts
Module 2: Edge AI Hardware – Microcontrollers and Edge Chips
Module 3: Neural Network Design for Constrained Devices
Module 4: Model Compression – Pruning, Quantization, Distillation
Module 5: TensorFlow Lite and PyTorch Mobile Frameworks
Module 6: Data Collection and Pre-processing for Edge AI
Module 7: Edge Deployment – Raspberry Pi, Arduino, and ESP32
Module 8: Real-Time Inference and Latency Optimization
Module 9: Edge AI Security and Privacy Considerations
Module 10: Capstone Project – End-to-End TinyML Application

Upon successful completion, learners will receive a Certificate of Completion from Uplatz, validating their expertise in Edge AI and TinyML. This Uplatz certification demonstrates your ability to design and deploy AI solutions on embedded and low-power devices, a critical skill in modern AI engineering.

The course content aligns with the TinyML Foundation Learning Path, preparing learners for industry-recognized credentials in embedded intelligence. This certification is ideal for data scientists, embedded developers, and AI engineers seeking to specialize in on-device ML, where performance and efficiency matter most.

Your certificate will confirm proficiency in deploying, optimizing, and maintaining AI models across diverse edge computing environments — from industrial IoT sensors to consumer devices.

With the rapid expansion of IoT and smart device networks, Edge AI and TinyML skills are in extremely high demand. By completing this course with Uplatz, you can pursue roles such as:

• Embedded AI Engineer

• TinyML Developer

• Edge Computing Architect

• IoT AI Specialist

• AI Firmware Developer

Professionals in this domain earn between $95 000 and $170 000 per year, depending on expertise and industry.

Career opportunities exist in smart manufacturing, autonomous systems, healthcare devices, robotics, and edge analytics companies. Edge AI engineers are essential to bridging the gap between intelligent algorithms and physical hardware — enabling faster, more private, and more sustainable AI deployments.

This course gives you the technical foundation to innovate at the intersection of AI and IoT, powering the next generation of connected, intelligent devices.

1. What is Edge AI?
   Running AI models directly on local devices instead of cloud servers for low latency and higher privacy.

2. What is TinyML?
   Deploying machine-learning models on ultra-low-power microcontrollers.

3. Why is quantization used in Edge AI?
   It reduces model size and computational cost with minimal accuracy loss.

4. What are the main frameworks for Edge AI development?
   TensorFlow Lite, PyTorch Mobile, and Edge Impulse.

5. What is the difference between Edge AI and Cloud AI?
   Edge AI processes data locally; Cloud AI relies on remote servers.

6. What is model pruning?
   Removing unnecessary parameters from a neural network to reduce complexity.

7. How is power consumption optimized in Edge AI?
   Through lightweight models, quantization, and hardware-aware design.

8. What are the limitations of TinyML?
   Restricted memory, compute power, and lack of floating-point operations.

9. Which hardware is commonly used for TinyML?
   Raspberry Pi, Arduino Nano 33 BLE Sense, and ARM Cortex-M series.

10. Why is Edge AI important for IoT applications?
    It enables real-time processing, reduces bandwidth use, and enhances data privacy.