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Course Outline
Introduction to Edge AI
- Definition and key concepts
- Differences between Edge AI and cloud AI
- Benefits and use cases of Edge AI
- Overview of edge devices and platforms
Setting Up the Edge Environment
- Introduction to edge devices (Raspberry Pi, NVIDIA Jetson, etc.)
- Installing necessary software and libraries
- Configuring the development environment
- Preparing the hardware for AI deployment
Developing AI Models for the Edge
- Overview of machine learning and deep learning models for edge devices
- Techniques for training models on local and cloud environments
- Model optimization 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 on various edge hardware
- 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: Building 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 latency and power consumption challenges
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
- An understanding of AI and machine learning concepts
- Experience with programming languages (Python recommended)
- Familiarity with edge computing concepts
Audience
- Developers
- Data scientists
- Tech enthusiasts
14 Hours
Delivery Options
Private Group Training
Our identity is rooted in delivering exactly what our clients need.
- Pre-course call with your trainer
- Customisation of the learning experience to achieve your goals -
- Bespoke outlines
- Practical hands-on exercises containing data / scenarios recognisable to the learners
- Training scheduled on a date of your choice
- Delivered online, onsite/classroom or hybrid by experts sharing real world experience
Private Group Prices RRP from €4560 online delivery, based on a group of 2 delegates, €1440 per additional delegate (excludes any certification / exam costs). We recommend a maximum group size of 12 for most learning events.
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Public Training
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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.
