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Duration 14 hours
Course Outline
Foundations of Edge AI
- Core definitions and essential concepts.
- Distinguishing Edge AI from cloud-based AI.
- Key advantages and applicable use cases.
- A survey of current edge devices and platforms.
Establishing the Edge Environment
- Introduction to specific edge hardware (e.g., Raspberry Pi, NVIDIA Jetson).
- Installation of required software and libraries.
- Configuration of the development workspace.
- Preparing hardware systems for AI workloads.
Creating AI Models for Edge Devices
- Overview of machine learning and deep learning architectures suitable for edge.
- Methods for training models in both local and cloud settings.
- Optimization strategies for edge deployment (including quantization and pruning).
- Essential tools and frameworks for Edge AI (such as TensorFlow Lite and OpenVINO).
Deploying Models on Edge Hardware
- Procedures for deploying AI models across different edge devices.
- Handling real-time data processing and inference on the edge.
- Techniques for monitoring and managing live models.
- Illustrative examples and industry case studies.
Practical AI Applications and Projects
- Building AI applications for edge platforms (e.g., computer vision, natural language processing).
- Project: Developing a smart camera system.
- Project: Implementing voice recognition on edge devices.
- Collaborative group exercises based on real-world scenarios.
Assessing and Optimizing Performance
- Methods for measuring model efficacy on edge devices.
- Utilizing tools for monitoring and debugging Edge AI apps.
- Tactics for enhancing AI model performance.
- Mitigating issues related to latency and power consumption.
Integrating with IoT Ecosystems
- Linking Edge AI solutions with IoT sensors and devices.
- Exploring communication protocols and data exchange techniques.
- Constructing a comprehensive Edge AI and IoT solution.
- Practical examples of system integration.
Ethical and Security Perspectives
- Safeguarding data privacy and security in Edge AI contexts.
- Mitigating bias and ensuring fairness in AI models.
- Ensuring compliance with relevant regulations and standards.
- Best practices for responsible AI deployment.
Practical Exercises and Capstone Projects
- Developing a complete Edge AI application.
- Engaging with real-world project scenarios.
- Collaborative group-based exercises.
- Presenting projects and receiving constructive feedback.
Requirements
- A solid grasp of AI and machine learning fundamentals.
- Proficiency in programming languages, with a recommendation for Python.
- A working knowledge of edge computing concepts.
Intended Audience
- Software Developers.
- Data Scientists.
- Technology Enthusiasts.
Testimonials (2)
Getting people that never used AI some repetition in prompting and people that do use AI to consider different methods to using it.
Matthew Gay - Tarsus Pharmaceuticals
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