Edge AI and Embedded Machine Learning Solutions Course

Training Outlines

Introduction

The "Edge AI and Embedded Machine Learning Solutions" course is designed to provide professionals with a comprehensive understanding of how AI can be integrated into edge devices and embedded systems. Over five days, participants will gain both theoretical knowledge and practical skills needed to deploy efficient machine learning models on constrained hardware platforms. This course is ideal for engineers, developers, and data scientists looking to expand their expertise in this rapidly evolving domain.

Objectives

• Understand the fundamentals of Edge AI and its applications.
• Learn the architecture and operation of embedded machine learning solutions.
• Explore various hardware and software tools for deploying AI at the edge.
• Design and implement efficient AI models on edge devices.
• Analyze the challenges and future trends in edge AI and embedded machine learning.

Course Outlines

Day 1: Introduction to Edge AI

• Overview of Edge AI and its significance
• Comparing Edge AI with cloud-based AI
• Real-world applications and case studies
• Hardware platforms for Edge AI
• Challenges and opportunities in deploying Edge AI

Day 2: Embedded Systems for AI

• Basics of embedded systems and microcontrollers
• IoT and its role in Edge AI
• Power constraints and efficiency in embedded systems
• Design considerations for AI on embedded systems
• Hands-on lab: Setting up an embedded system environment

Day 3: Tools and Frameworks

• Overview of AI software frameworks
• Introduction to TensorFlow Lite and PyTorch Mobile
• Using ONNX Runtime for model deployment
• Toolchains and compilers for embedding AI models
• Hands-on lab: Converting models for edge deployment

Day 4: Designing and Deploying Models

• Model compression techniques for edge deployment
• Quantization and pruning techniques
• Real-time data processing on edge devices
• Strategies for edge model optimization
• Hands-on lab: Deploying a machine learning model on an edge device

Day 5: Challenges and Future Trends

• Security and privacy concerns in Edge AI
• Maintaining accuracy in constrained environments
• AI model lifecycle management on edge devices
• Future trends in Edge AI and embedded machine learning
• Capstone project: Design a complete AI solution for an edge use case

Training Schedule

Below is the table of cities along with the respective dates for the upcoming training sessions of Edge AI and Embedded Machine Learning Solutions Course. Please review the schedule to find the most convenient option for you. You can also use the below search bar to type the city name and filter the results.