Course Outline
Introduction to Edge AI for Government
- Definition and key concepts
- Differences between Edge AI and cloud AI
- Benefits and use cases of Edge AI in government operations
- Overview of edge devices and platforms suitable for government applications
Setting Up the Edge Environment for Government
- Introduction to edge devices (Raspberry Pi, NVIDIA Jetson, etc.) for government use
- Installing necessary software and libraries in compliance with government standards
- Configuring the development environment to meet public sector requirements
- Preparing the hardware for AI deployment in government settings
Developing AI Models for the Edge for Government
- Overview of machine learning and deep learning models suitable for edge devices in government contexts
- Techniques for training models on local and cloud environments, adhering to government data policies
- Model optimization for edge deployment (quantization, pruning, etc.) to ensure efficiency in public sector applications
- Tools and frameworks for Edge AI development (TensorFlow Lite, OpenVINO, etc.) that align with government standards
Deploying AI Models on Edge Devices for Government
- Steps for deploying AI models on various edge hardware in government settings
- Real-time data processing and inference on edge devices, ensuring compliance with public sector workflows
- Monitoring and managing deployed models to maintain accountability and governance
- Practical examples and case studies relevant to government operations
Practical AI Solutions and Projects for Government
- Developing AI applications for edge devices (e.g., computer vision, natural language processing) tailored for government needs
- Hands-on project: Building a smart camera system for government facilities
- Hands-on project: Implementing voice recognition on edge devices for public sector use
- Collaborative group projects and real-world scenarios in the context of government operations
Performance Evaluation and Optimization for Government
- Techniques for evaluating model performance on edge devices, ensuring they meet government standards
- Tools for monitoring and debugging edge AI applications in a public sector environment
- Strategies for optimizing AI model performance to enhance efficiency in government operations
- Addressing latency and power consumption challenges in government settings
Integration with IoT Systems for Government
- Connecting edge AI solutions with IoT devices and sensors in government infrastructure
- Communication protocols and data exchange methods suitable for public sector use
- Building an end-to-end Edge AI and IoT solution for government applications
- Practical integration examples relevant to government projects
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 deployed by the government
- Compliance with regulations and standards specific to public sector operations
- Best practices for responsible AI deployment in government contexts
Hands-On Projects and Exercises for Government
- Developing a comprehensive Edge AI application tailored for government use
- Real-world projects and scenarios relevant to public sector operations
- Collaborative group exercises focusing on government-specific challenges
- Project presentations and feedback in the context of government requirements
Summary and Next Steps for Government
Requirements
- A comprehensive understanding of artificial intelligence and machine learning concepts for government applications
- Practical experience with programming languages, with Python being highly recommended for government projects
- Knowledge of edge computing principles and their relevance to public sector operations
Audience
- Developers for government initiatives
- Data scientists working in the public sector
- Technology enthusiasts interested in government applications
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.
