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Course Outline
Introduction to Edge AI and IoT
- Defining Edge AI and its core concepts.
- An overview of IoT system structures and designs.
- Analyzing the advantages and hurdles of merging Edge AI with IoT.
- Examining practical real-world applications and scenarios.
Edge AI Architecture for IoT
- Identifying the key components of Edge AI systems in IoT.
- Reviewing hardware and software prerequisites.
- Mapping data flows in Edge AI-driven IoT applications.
- Strategies for integrating with existing IoT infrastructures.
Preparing the Edge AI and IoT Environment
- Introduction to leading IoT platforms (e.g., Arduino, Raspberry Pi, NVIDIA Jetson).
- Installing required software packages and libraries.
- Configuring the development workspace.
- Initiating the Edge AI and IoT setup process.
Building AI Models for IoT Devices
- Survey of machine learning and deep learning models suited for edge and IoT.
- Training and fine-tuning models for IoT deployment.
- Utilizing key frameworks for Edge AI development (TensorFlow Lite, OpenVINO, etc.).
- Methods for compressing and optimizing model performance.
Data Management and Preprocessing in IoT
- Techniques for gathering data in IoT contexts.
- Preprocessing and augmenting data for edge hardware.
- Overseeing data pipelines on IoT devices.
- Safeguarding data privacy and security within IoT networks.
Releasing Edge AI Models on IoT Devices
- Procedures for deploying AI models onto IoT edge hardware.
- Methods for overseeing and managing live models.
- Performing real-time inference and data processing on IoT devices.
- Reviewing case studies and practical deployment examples.
Connecting Edge AI with IoT Protocols and Platforms
- Survey of IoT communication standards (MQTT, CoAP, HTTP, etc.).
- Linking Edge AI solutions with IoT sensors and actuators.
- Constructing complete End-to-End Edge AI and IoT solutions.
- Practical demonstrations and use cases.
Use Cases and Applications
- Sector-specific applications of Edge AI in IoT.
- Detailed case studies in smart homes, industrial IoT, healthcare, and beyond.
- Insights from successful implementations and lessons learned.
- Emerging trends and future opportunities in Edge AI for IoT.
Ethical Considerations and Best Practices
- Protecting privacy and security in Edge AI and IoT deployments.
- Mitigating bias and ensuring fairness in AI models.
- Adhering to regulatory frameworks and industry standards.
- Best practices for responsible AI integration in IoT.
Hands-On Projects and Exercises
- Engineering a sophisticated Edge AI application for IoT.
- Working on real-world projects and scenarios.
- Participating in collaborative group exercises.
- Presenting projects and receiving feedback.
Summary and Next Steps
Requirements
- A solid grasp of fundamental AI and machine learning principles.
- Proficiency in programming, with a recommended background in Python.
- Familiarity with IoT frameworks and associated technologies.
Target Audience
- Developers specializing in IoT.
- System architects.
- Professionals working within the industry.
14 Hours
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