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Course Outline
Introduction to the Synergy of Edge AI and IoT
- Defining Edge AI and exploring its core tenets.
- A broad view of IoT system designs and architectures.
- Examining the advantages and obstacles of combining Edge AI with IoT.
- Analyzing practical real-world applications and use cases.
Architectural Design for Edge AI in IoT
- Identifying the key components of IoT-focused Edge AI systems.
- Specifying necessary hardware and software prerequisites.
- Mapping data flows within IoT applications enabled by Edge AI.
- Strategies for integrating with pre-existing IoT infrastructures.
Preparing the Edge AI and IoT Workspace
- Introduction to leading IoT platforms (such as Arduino, Raspberry Pi, and NVIDIA Jetson).
- Installation of essential software tools and libraries.
- Setting up and optimizing the development workspace.
- Initializing the configuration for Edge AI and IoT development.
Creating AI Models for IoT Hardware
- Overview of machine learning and deep learning models suited for edge and IoT contexts.
- Training and fine-tuning models for efficient IoT deployment.
- Utilization of specialized tools and frameworks (e.g., TensorFlow Lite, OpenVINO).
- Methods for compressing and optimizing model performance.
Handling Data Management and Preprocessing in IoT
- Techniques for gathering data within IoT ecosystems.
- Preprocessing and augmenting data specifically for edge devices.
- Overseeing data pipelines on IoT hardware.
- Prioritizing data privacy and security within IoT settings.
Rolling Out Edge AI Models on IoT Hardware
- Detailed procedures for deploying AI models to IoT edge devices.
- Methods for monitoring and maintaining active models.
- Executing real-time data processing and inference on IoT devices.
- Reviewing case studies and practical deployment examples.
Connecting Edge AI with IoT Protocols and Platforms
- Exploration of IoT communication standards (including MQTT, CoAP, HTTP).
- Linking Edge AI solutions with IoT sensors and actuators.
- Constructing complete end-to-end Edge AI and IoT solutions.
- Examining specific practical scenarios and applications.
Practical Use Cases and Industry Applications
- Application of Edge AI in IoT across specific industries.
- Detailed case studies in smart homes, industrial IoT, and healthcare.
- Learning from success stories and past experiences.
- Forecasting future trends and emerging opportunities.
Ethical Frameworks and Operational Best Practices
- Safeguarding privacy and security in Edge AI and IoT deployments.
- Mitigating bias and ensuring fairness in AI models.
- Maintaining compliance with relevant regulations and industry standards.
- Adopting best practices for responsible AI integration in IoT.
Practical Projects and Skill-Building Exercises
- Engineering a sophisticated Edge AI application for IoT.
- Working through real-world projects and simulated scenarios.
- Participating in collaborative group-based exercises.
- Presenting projects and receiving constructive feedback.
Summary and Path Forward
Requirements
- A solid grasp of fundamental AI and machine learning principles.
- Proficiency in programming, with Python being the recommended language.
- Familiarity with core IoT concepts and underlying technologies.
Target Audience
- IoT Developers.
- System Architects.
- Industry Professionals.
14 Hours
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