Edge Ai & Embedded Machine Learning Training Course: Deploy Ml On Edge Devices

Introduction

Unlock the potential of localized AI with our Edge AI and Embedded Machine Learning Training Course. This program is designed to equip you with the essential skills to deploy machine learning models on edge devices, enabling you to create intelligent, responsive, and efficient applications. In today's IoT-driven world, mastering edge AI is crucial for processing data closer to its source, reducing latency, and enhancing privacy. Our edge AI training course offers hands-on experience and expert guidance, empowering you to implement cutting-edge embedded machine learning solutions.

This embedded machine learning training delves into the core concepts of deploying ML models on resource-constrained devices, covering topics such as model optimization, hardware acceleration, and real-time inference. You'll gain expertise in using industry-standard tools and techniques to deploy machine learning models on edge devices, meeting the demands of modern IoT and embedded systems projects. Whether you're an embedded systems engineer, AI developer, or IoT professional, this Edge AI & Embedded Machine Learning course will empower you to build and deploy intelligent edge applications.

Target Audience:

• Embedded Systems Engineers
• AI Developers
• IoT Professionals
• Data Scientists
• Hardware Engineers
• Software Developers
• Anyone needing edge AI and embedded ML skills

Course Objectives:

• Understand the fundamentals of edge AI and embedded machine learning.
• Master model optimization techniques for edge deployment.
• Utilize hardware acceleration for efficient inference on edge devices.
• Implement real-time inference on resource-constrained devices.
• Design and build embedded machine learning applications.
• Optimize edge AI solutions for power efficiency and latency.
• Troubleshoot and address common challenges in edge AI deployment.
• Implement model deployment and management on edge devices.
• Integrate edge AI with real-world IoT and embedded systems.
• Understand how to handle data privacy and security in edge AI applications.
• Explore advanced edge AI techniques (e.g., federated learning, on-device training).
• Apply real world use cases for edge AI and embedded machine learning.
• Leverage edge AI frameworks and tools for efficient development.

Duration

10 Days

Course content

Module 1: Introduction to Edge AI and Embedded Machine Learning

• Fundamentals of edge AI and embedded machine learning.
• Overview of model optimization, hardware acceleration, and real-time inference.
• Setting up an edge AI development environment.
• Introduction to edge AI frameworks and tools.
• Best practices for edge AI development.

Module 2: Model Optimization for Edge Deployment

• Implementing model optimization techniques for edge deployment.
• Utilizing quantization, pruning, and knowledge distillation.
• Designing and building optimized models for resource-constrained devices.
• Optimizing models for size and computational efficiency.
• Best practices for model optimization.

Module 3: Hardware Acceleration for Inference

• Implementing hardware acceleration for efficient inference.
• Utilizing specialized hardware (TPUs, GPUs, embedded processors).
• Designing and building hardware-accelerated inference pipelines.
• Optimizing inference for specific hardware platforms.
• Best practices for hardware acceleration.

Module 4: Real-Time Inference on Edge Devices

• Implementing real-time inference on resource-constrained devices.
• Utilizing efficient inference engines (TensorFlow Lite, ONNX Runtime).
• Designing and building real-time data processing pipelines.
• Optimizing inference for low latency and high throughput.
• Best practices for real-time inference.

Module 5: Embedded Machine Learning Applications

• Designing and building embedded machine learning applications.
• Implementing ML models for sensor data processing.
• Utilizing edge AI for computer vision and audio processing.
• Optimizing applications for edge device constraints.
• Best practices for embedded applications.

Module 6: Edge AI Optimization

• Optimizing edge AI solutions for power efficiency and latency.
• Utilizing power management techniques and low-power hardware.
• Implementing efficient data processing and communication protocols.
• Designing scalable edge AI architectures.
• Best practices for edge AI optimization.

Module 7: Troubleshooting Edge AI Challenges

• Debugging common challenges in edge AI deployment.
• Analyzing model performance and hardware limitations.
• Utilizing troubleshooting techniques for problem resolution.
• Resolving common edge AI issues.
• Best practices for troubleshooting.

Module 8: Model Deployment and Management

• Implementing model deployment and management on edge devices.
• Utilizing over-the-air (OTA) updates and remote management.
• Designing and building model deployment pipelines.
• Optimizing deployment for scalability and reliability.
• Best practices for model deployment.

Module 9: Integration with IoT and Embedded Systems

• Integrating edge AI with real-world IoT and embedded systems.
• Utilizing communication protocols (MQTT, CoAP).
• Designing and building edge-to-cloud solutions.
• Optimizing integration for data exchange and control.
• Best practices for integration.

Module 10: Data Privacy and Security

• Implementing data privacy and security in edge AI applications.
• Utilizing secure communication and encryption techniques.
• Designing and building privacy-preserving edge AI solutions.
• Optimizing data handling for compliance.
• Best practices for privacy.

Module 11: Advanced Edge AI Techniques

• Exploring advanced edge AI techniques (federated learning, on-device training).
• Utilizing federated learning for distributed model training.
• Implementing on-device training for adaptive edge AI.
• Designing and building advanced edge AI solutions.
• Optimizing advanced techniques for specific applications.
• Best practices for advanced techniques.

Module 12: Real-World Use Cases

• Implementing edge AI for smart home automation.
• Utilizing edge AI for industrial IoT and predictive maintenance.
• Implementing edge AI for autonomous vehicles and robotics.
• Utilizing edge AI for healthcare monitoring and diagnostics.
• Best practices for real-world applications.

Module 13: Edge AI Frameworks and Tools Implementation

• Utilizing TensorFlow Lite, Edge Impulse, and other edge AI frameworks.
• Implementing edge AI models with specific tools.
• Designing and building edge AI pipelines.
• Optimizing tool usage for efficient development.
• Best practices for tool implementation.

Module 14: Performance Evaluation and Monitoring

• Implementing performance evaluation and monitoring for edge AI.
• Utilizing metrics for latency, power consumption, and model accuracy.
• Designing and building monitoring dashboards.
• Optimizing monitoring for real-time insights.
• Best practices for monitoring.

Module 15: Future Trends in Edge AI

• Emerging trends in edge AI and embedded machine learning.
• Utilizing AI for edge-based data fusion and sensor processing.
• Implementing AI for edge-to-cloud collaboration.
• Best practices for future edge AI applications.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 7 working days before commencement of the training.