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Plan Szkolenia
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
Wymagania
- 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 godzin
Opinie uczestników (2)
ekosystem ML nie tylko MLFlow, ale także Optuna, hyperops, docker i docker-compose
Guillaume GAUTIER - OLEA MEDICAL
Szkolenie - MLflow
Przetłumaczone przez sztuczną inteligencję
Uczestniczenie w szkoleniu Kubeflow, które odbyło się zdalnie, było dla mnie bardzo przyjemne. To szkolenie pozwoliło mi ugruntować moją wiedzę dotyczącą usług AWS, K8s oraz wszystkich narzędzi devOps związanych z Kubeflow, które są niezbędnymi podstawami do właściwego podejścia do tematu. Chciałbym podziękować Malawskiemu Marcinowi za jego cierpliwość i profesjonalizm w trakcie szkolenia oraz za rady dotyczące najlepszych praktyk. Malawski podejmuje temat z różnych perspektyw, różnych narzędzi wdrażania Ansible, EKS kubectl, Terraform. Teraz jestem zdecydowanie przekonany, że wybieram właściwy kierunek aplikacji.
Guillaume Gautier - OLEA MEDICAL | Improved diagnosis for life TM
Szkolenie - Kubeflow
Przetłumaczone przez sztuczną inteligencję
