Course Outline
Introduction to Edge AI for Government
- Definition and key concepts of Edge AI
- Differences between Edge AI and cloud AI in government operations
- Benefits and use cases of Edge AI for government applications
- Overview of edge devices and platforms suitable for government use
Setting Up the Edge Environment for Government
- Introduction to edge devices (Raspberry Pi, NVIDIA Jetson, etc.) for government deployment
- Installing necessary software and libraries for secure government operations
- Configuring the development environment to meet government standards
- Preparing the hardware for AI deployment in government settings
Developing AI Models for the Edge in Government
- Overview of machine learning and deep learning models suitable for edge devices in government applications
- Techniques for training models on local and cloud environments, ensuring compliance with government regulations
- Model optimization for edge deployment (quantization, pruning, etc.) to enhance efficiency for government use
- Tools and frameworks for Edge AI development in government (TensorFlow Lite, OpenVINO, etc.)
Deploying AI Models on Edge Devices for Government
- Steps for deploying AI models on various edge hardware to support government operations
- Real-time data processing and inference capabilities on edge devices in government contexts
- Monitoring and managing deployed models to ensure reliability and security for government applications
- Practical examples and case studies of Edge AI deployment in government settings
Practical AI Solutions and Projects for Government
- Developing AI applications for edge devices tailored to government needs (e.g., computer vision, natural language processing)
- Hands-on project: Building a smart camera system for enhanced security in government facilities
- Hands-on project: Implementing voice recognition on edge devices for improved accessibility and communication in government offices
- Collaborative group projects and real-world scenarios to address specific government challenges
Performance Evaluation and Optimization for Government
- Techniques for evaluating model performance on edge devices to meet government standards
- Tools for monitoring and debugging edge AI applications in a secure government environment
- Strategies for optimizing AI model performance to enhance efficiency in government operations
- Addressing latency and power consumption challenges to ensure reliable government services
Integration with IoT Systems for Government
- Connecting edge AI solutions with IoT devices and sensors for comprehensive government systems
- Communication protocols and data exchange methods suitable for government use
- Building an end-to-end Edge AI and IoT solution to support government operations
- Practical integration examples in government settings
Ethical and Security Considerations for Government
- Ensuring data privacy and security in Edge AI applications for government use
- Addressing bias and fairness in AI models to promote equitable government services
- Compliance with regulations and standards for responsible government AI deployment
- Best practices for ethical and secure AI implementation in government settings
Hands-On Projects and Exercises for Government
- Developing a comprehensive Edge AI application tailored to government needs
- Real-world projects and scenarios relevant to government operations
- Collaborative group exercises to foster teamwork in government settings
- Project presentations and feedback sessions to enhance learning for government personnel
Summary and Next Steps for Government
Requirements
- A solid understanding of artificial intelligence and machine learning concepts for government applications
- Experience with programming languages, with Python being highly recommended
- Familiarity with edge computing principles
Audience
- Developers for government projects
- 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.
