It seems this is focused on on-device computation - as distinct from, say, Cloudflare's definition of the "edge" as a smart CDN with an ability to run arbitrary code and AI models in geographically distributed data centers (https://workers.cloudflare.com/).
> EdgeAI represents a paradigm shift in artificial intelligence deployment, bringing AI capabilities directly to edge devices rather than relying solely on cloud-based processing. This approach enables AI models to run locally on devices with limited computational resources, providing real-time inference capabilities without requiring constant internet connectivity.
I suppose that the definition "edge is anything except a central data center" is consistent between these two approaches, and there's overlap in needing reliable ways to deploy code to less-trusted/less-centrally-controlled environments... but it certainly muddies the techniques involved.
At this rate of term overloading, the next thing you know we'll be using the word "edgy" to describe teenagers or something...
No, edge is just poorly defined. Plenty of companies call their servers “edge” because they’re collocated with ISPs. Even ISPs when they talk about edge compute aren’t talking about your laptop but about compute in their colo.
maybe a decent definition could be compute as close to the user latency-wise as practically possible while having full access to the necessary data.
For certain things this will be able to go as far as the device if you're only ever operating on data the user fully owns, other things will need data centers still but just decentralised and closer to the user via fancier architectures ala the Cloudflare model.
In GPU compute land, "edge" means on the consumer device. The latency of delivery is negligible in comparison to the wall clock compute demands, so it doesn't make much sense to park your GPUs near the consumer.
IoT is "edge".
The only place I've seen "edge" used otherwise is in delivery of large files, e.g. ISP-colocated video delivery.
One of the most common uses for edge AI not listed in this course is computer vision. You similarly want real-time inference for processing video. Another open source project that makes it easy to use SOTA vision models on the edge is inference: https://github.com/roboflow/inference
> Welcome to EdgeAI for Beginners – your comprehensive...
Em dash and the word "comprehensive", nearly 100% proof the document was written by AI.
I use AI daily for my job, so I am not against its use, but recently if I detect some prose is written by AI it's hard for me to finish it. The written word is supposed to be a window into someone's thoughts, and it feels almost like a broken social contract to substitute an AI's "thoughts" here instead.
AI generated prose should be labeled as such, it's the decent thing to do.
Or just by somebody that knows how to use English punctuation properly.
Is it so hard to believe that there are some people in the world capable of hitting option + “-“ on their keyboard (or simply let their editor do it for them)?
I said em dash _and_ the word comprehensive. If you work with LLM generated text enough it gets very easy to see the telltale signs. The emojis at the start of each row in the table are also a dead giveaway.
I am guessing you are one of those people who used em dashes before LLMs came out and are now bitter they are an indicator of LLMs. If that's the case, I am sorry for the situation you find yourself in.
Isn’t edge AI just a way to deploy AI to meet product requirements? What is special about this course? Is Microsoft trying to sell this as a service? If so what is the revenue model and hardware used?
Edit: seems like it's like that in most languages lol, at least those with a latin script
Per Microsoft's definition in https://github.com/microsoft/edgeai-for-beginners/blob/main/...:
> EdgeAI represents a paradigm shift in artificial intelligence deployment, bringing AI capabilities directly to edge devices rather than relying solely on cloud-based processing. This approach enables AI models to run locally on devices with limited computational resources, providing real-time inference capabilities without requiring constant internet connectivity.
(This isn't necessarily just Microsoft's definition - https://www.redhat.com/en/topics/edge-computing/what-is-edge... from 2023 defines edge computing as on-device as well, and is cited in https://en.wikipedia.org/wiki/Edge_computing#cite_note-35)
I suppose that the definition "edge is anything except a central data center" is consistent between these two approaches, and there's overlap in needing reliable ways to deploy code to less-trusted/less-centrally-controlled environments... but it certainly muddies the techniques involved.
At this rate of term overloading, the next thing you know we'll be using the word "edgy" to describe teenagers or something...
For certain things this will be able to go as far as the device if you're only ever operating on data the user fully owns, other things will need data centers still but just decentralised and closer to the user via fancier architectures ala the Cloudflare model.
IoT is "edge".
The only place I've seen "edge" used otherwise is in delivery of large files, e.g. ISP-colocated video delivery.
> Welcome to EdgeAI for Beginners – your comprehensive...
Em dash and the word "comprehensive", nearly 100% proof the document was written by AI.
I use AI daily for my job, so I am not against its use, but recently if I detect some prose is written by AI it's hard for me to finish it. The written word is supposed to be a window into someone's thoughts, and it feels almost like a broken social contract to substitute an AI's "thoughts" here instead.
AI generated prose should be labeled as such, it's the decent thing to do.
Is it so hard to believe that there are some people in the world capable of hitting option + “-“ on their keyboard (or simply let their editor do it for them)?
I am guessing you are one of those people who used em dashes before LLMs came out and are now bitter they are an indicator of LLMs. If that's the case, I am sorry for the situation you find yourself in.
I've been wondering why LLMs seem to prefer the em dash over en dash as I feel like en (or hyphen) is used more frequently in modern text.
This is a course on how to use Microsoft compute to maximise their profits