Course Outline
Introduction to Edge AI
- Definition and key concepts.
- Distinguishing features between Edge AI and cloud-based AI.
- Benefits and practical use cases of Edge AI.
- Overview of edge devices and platforms.
Setting Up the Edge Environment
- Introduction to edge devices (e.g., Raspberry Pi, NVIDIA Jetson).
- Installation of required software and libraries.
- Configuration of the development environment.
- Preparing hardware for AI deployment.
Developing AI Models for the Edge
- Overview of machine learning and deep learning models suitable for edge devices.
- Techniques for training models in local and cloud environments.
- Model optimization techniques for edge deployment (quantization, pruning, etc.).
- Tools and frameworks for Edge AI development (TensorFlow Lite, OpenVINO, etc.).
Deploying AI Models on Edge Devices
- Steps for deploying AI models across various edge hardware platforms.
- Real-time data processing and inference on edge devices.
- Monitoring and managing deployed models.
- Practical examples and case studies.
Practical AI Solutions and Projects
- Developing AI applications for edge devices (e.g., computer vision, natural language processing).
- Hands-on project: Constructing a smart camera system.
- Hands-on project: Implementing voice recognition on edge devices.
- Collaborative group projects and real-world scenarios.
Performance Evaluation and Optimization
- Techniques for evaluating model performance on edge devices.
- Tools for monitoring and debugging edge AI applications.
- Strategies for optimizing AI model performance.
- Addressing challenges related to latency and power consumption.
Integration with IoT Systems
- Connecting edge AI solutions with IoT devices and sensors.
- Communication protocols and data exchange methods.
- Building an end-to-end Edge AI and IoT solution.
- Practical integration examples.
Ethical and Security Considerations
- Ensuring data privacy and security in Edge AI applications.
- Addressing bias and fairness in AI models.
- Compliance with regulations and standards.
- Best practices for responsible AI deployment.
Hands-On Projects and Exercises
- Developing a comprehensive Edge AI application.
- Real-world projects and scenarios.
- Collaborative group exercises.
- Project presentations and feedback.
Summary and Next Steps
Requirements
- A foundational understanding of AI and machine learning concepts.
- Proficiency in programming languages (Python is recommended).
- Familiarity with edge computing principles.
Target Audience
- Software Developers.
- Data Scientists.
- Technology Enthusiasts.
Testimonials (2)
the ML ecosystem not only MLFlow but Optuna, hyperops, docker , docker-compose
Guillaume GAUTIER - OLEA MEDICAL
Course - MLflow
I enjoyed participating in the Kubeflow training, which was held remotely. This training allowed me to consolidate my knowledge for AWS services, K8s, all the devOps tools around Kubeflow which are the necessary bases to properly tackle the subject. I wanted to thank Malawski Marcin for his patience and professionalism for training and advice on best practices. Malawski approaches the subject from different angles, different deployment tools Ansible, EKS kubectl, Terraform. Now I am definitely convinced that I am going into the right field of application.
