This LIVE online event will take place on Thursday 1st October
We're only just starting to understand the true potential of technology at the intersection of Artificial Intelligence, MLOps (Machine Learning & DevOps), Cloud Computing and Edge Computing - but the possibilities are endless.
For the past few years, our AI Engineers, Data Scientists and Cloud Advocates at Microsoft have been working with the University of Oxford to further the development of these technologiesandfurther the practical application of these in the world.
The collaboration has resulted in the University of Oxford offering specific courses on AI and Cloud/Edge computing - and on the development of theAI Edge EngineerLearning Pathon Microsoft Learn.
The interplay between AI, cloud, and edge is a rapidly evolving domain. Currently, many IoT solutions are based on basic telemetry. The telemetry function captures data from edge devices and stores it in a data store. Our approach extends beyond basic telemetry. We aim to model problems in the real world through machine learning and deep learning algorithms and implement the model through AI and Cloud on to edge devices. The model is trained in the cloud and deployed on the edge device. The deployment to the edge provides a feedback loop to improve the business process (digital transformation).
In the AI Edge Engineering learning path, we take an interdisciplinary engineering approach. We aspire to create a standard template for many complex areas for deployment of AI on edge devices such as Drones, Autonomous vehicles etc. The learning path presents implementation strategies for an evolving landscape of complex AI applications. Containers are central to this approach. When deployed to edge devices, containers can encapsulate deployment environments for a range of diverse hardware. CICD (Continuous integration - continuous deployment) is a logical extension to deploying containers on edge devices. In future modules in this learning path, we may include other techniques such as serverless computing and deployment on Microcontroller Units.
The engineering-led approach underpins themes / pedagogies for engineering education such as
Experimentation and Problem solving
Improving through experimentation
Deployment and analysis through testing
Impact on other engineering domains
Forecasting behaviour of a component or system
Working within constraints/tolerances and specific operating conditions – for example, device constraints
Safety and security considerations
Building tools which help to create the solution
Improving processes - Using edge(IoT) to provide an analytics feedback loop to the business process to drive processes
The societal impact of engineering
The aesthetical impact of design and engineering
Deployments at scale
Solving complex business problems by an end-to-end deployment of AI, edge, and cloud.
Ultimately, AI, cloud, and edge technologies deployed as containers in CICD mode can transform whole industries by creating an industry-specific, self-learning ecosystem spanning the entire value chain. We aspire to design such a set of templates/methodologies for the deployment of AI to edge devices in the context of the cloud. In this learning path, you will:
Learn about creating solutions using IoT and the cloud
Understand the process of deploying IoT based solutions on edge devices
Learn the process of implementing models to edge devices using containers
Now, we're bringing together the team at Microsoft and the academics at University of Oxford that worked to build this learning path - and you can meet them and find out more about this free Learning Path, as well as some of the amazing applications of these technologies, at our event on 1st October.