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Duration 14 hours
Course Outline
Fundamentals of Edge AI
- Defining core concepts and terminology
- Distinguishing between Edge AI and Cloud AI architectures
- Exploring the advantages and typical use cases of Edge AI
- Surveying available edge devices and platforms
Configuring the Edge Environment
- Getting to know edge hardware such as Raspberry Pi and NVIDIA Jetson
- Installing required software and libraries
- Setting up the development workspace
- Preparing hardware infrastructure for AI workloads
Creating AI Models for Edge Constraints
- Reviewing machine learning and deep learning architectures suitable for edge
- Methods for training models in both local and cloud environments
- Optimizing models for edge performance using quantization and pruning
- Utilizing frameworks like TensorFlow Lite and OpenVINO for Edge AI
Deployment of AI Models on Edge Hardware
- Process for deploying models across different edge devices
- Handling real-time data processing and inference
- Ongoing monitoring and management of deployed models
- Examining practical examples and case studies
Practical Implementations and Projects
- Building AI applications for edge, such as computer vision and NLP
- Project: Creating an intelligent camera system
- Project: Implementing voice recognition on edge devices
- Team-based projects simulating real-world conditions
Assessing and Tuning Performance
- Methods for benchmarking model performance on edge hardware
- Using tools to monitor and debug Edge AI applications
- Strategies for enhancing model efficiency
- Mitigating challenges related to latency and energy consumption
Connecting with IoT Ecosystems
- Integrating Edge AI with IoT sensors and devices
- Understanding communication protocols and data exchange
- Constructing a complete Edge AI and IoT solution
- Real-world integration demonstrations
Ethical and Security Frameworks
- Safeguarding data privacy and security in Edge AI
- Mitigating bias and ensuring fairness in AI models
- Adhering to relevant regulations and industry standards
- Best practices for responsible AI rollout
Capstone Projects and Practice
- Building a comprehensive Edge AI application
- Engaging with real-world project scenarios
- Collaborative group assignments
- Presenting projects and receiving constructive feedback
Requirements
- A foundational grasp of AI and machine learning theories.
- Proficiency in programming languages (Python is preferred).
- Basic familiarity with the concepts of edge computing.
Target 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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