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Who should attend
This is an intermediate course, suitable for professionals with several years of experience, with an interest or requirement to understand digital marketing and social engagement.
It is applicable for professionals engaged in the following areas.
- Software Architects
- Senior Software Engineers
About the course
This 4-day programme is intended for anyone who wishes to gain specialised knowledge in designing intelligent Edge Computing systems. This course will benefit those working in medical, manufacturing, defence and other industry types. Participants will gain in-depth knowledge as well as practical skills through projects and assessment that reinforce their learning and engage their newly acquired knowledge.
- Design Edge compute systems to provide multi-level intelligence for IoT, transducers and other devices.
- Build data collection, analytics, and decision-making capabilities into these Edge compute systems.
- Integrate machine learning and analytics into Edge Computing to perform decision-making, self-healing, and self-learning This course is part of the Software Systems series and Graduate Certificate in Architecting Smart Systems series offered by NUS-ISS.
Other related courses you might also want to consider are: * NICF - Statistics Bootcamp (SF) * NICF - Python for Data, Ops and Things (SF)
What Will Be Covered
The outline teaching agenda is as follows:
Day 1: Design and Orchestration for Multi-Layer Edge Computing
- Methodologies and key principles in designing integrated Edge Computing sensor networks
- Capabilities of Edge Computing nodes, sensors, transducers and related technologies
- Fog architecture and cloud-based IoT orchestration patterns/practices for Edge Computing systems
- Platforms and technologies for configuring and orchestrating Edge Computing topologies
- Industrial use case / hands-on exercise to design a layered Edge Computing system Day 2: Data Collection, Analytics, and Decision-making in Edge Computing
- Industry tools and technologies for data collection, transmission, and analytics for IoT
- Range of statistical and machine learning modeling techniques, such as linear and logistic regression
- Features, pros and cons of the statistical approaches, algorithms and tools
- Industrial use case / hands-on exercise applying data collection and analysis techniques to a real-world Edge Computing system
Day 3: Self-monitoring and Self-healing in Edge Computing
- Quality of Service monitoring, fault management, and self-healing for IoT
- Advanced mathematical modelling techniques such as convolutional or recurrent neural networks.
- Industrial use case / hands-on exercise to design a self-monitoring and healing Edge Computing system Day 4: Self-learning and Adaptation for Edge Computing
- Self-learning models such as reinforcement learning and online learning algorithms
- Selection and optimization of advanced models for self-learning Edge computing applications
- Industrial use case / hands-on exercise on designing a self-learning Edge Computing system
Lisa is with the Software Engineering and Design Practice, StackUp program for National University of Singapore, Institute of Systems Science (NUS-ISS). Lisa has multiple years of extensive experience in software product research and development at Microsoft Corporation (USA). Her background in...
Because of COVID-19, many providers are cancelling or postponing in-person programs or providing online participation options.
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