Edge AI is not killing the cloud

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Edge AI is the next stage in the evolution of cloud-native applications over the internet. In this post, we explain you about the topic "Edge AI is not killing the cloud" which distinguishes the perspective of industry. To know more, feel free to reach us at https://visailabs.com/

Technology

Edge AI is not
killing the Cloud
Rather, Edge AI
heralds the coming of
age for Cloud AI

Cloud AI now has hyper-
scalable infra, computing
power & powerful AI
models to decipher
anything
But the missing link
is data...

Data is collected
on the edge
Edge AI pulls the data
center closer to where
the data is created

We process data &
do analytics on the
edge
In the new normal, we
don’t ship all data to the
cloud

Big Data Analytics
is done on the cloud
Edge AI only creates
Event based data on the
edge

The future of AI is
not "Cloud only" or
"Edge only"
It will have an "Edge
first, Cloud next"
mentality

3 Major reasons
why Edge first
is the
Future of AI

The overall cost of sending video
data to the cloud and processing
it there is high.
Edge reduces data sent and
computing to be done on the
cloud
Why Edge AI?
Cost
11

Latency is defined as the time
difference btw. input and output
Latency sensitive applications
such as Accident avoidance can’t
wait for sensor inputs
to move to the cloud and revert
Why Edge AI?
Latency
22

Why Edge AI?
Privacy
The increase of audio and video
sensors has put everyone on
edge.
With numerous instances of
“switched off” devices listening to
conversations processing on edge
reduces privacy concerns
33

If edge is so good,
why send data to
cloud?
Can’t we do all on edge?

Edge AI applications such
as IOT & sensors collect
data from a single physical
point and process it.
Edge AI focuses on
collecting end-point based
event driven data

In the cloud, we
aggregate the event
data from edge,
derive insights and
glean accurate
forecasts

The future of AI is
Edge-Cloud Hybrid
Edge AI creates end-point
based event driven data
Cloud aggregates and
processes the shared edge
data

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The proliferation of Internet of Things (IoT) and the success of rich cloud services have pushed the horizon of a new computing paradigm, edge computing, which calls for processing the data at the edge of the network. Edge computing has the potential to address the concerns of response time requirement, battery life constraint, bandwidth cost saving, as well as data safety and privacy. In this paper, we introduce the definition of edge computing, followed by several case studies, ranging from cloud offloading to smart home and city, as well as collaborative edge to materialize the concept of edge computing. Finally, we present several challenges and opportunities in the field of edge computing, and hope this paper will gain attention from the community and inspire more research in this direction. Edge computing refers to the enabling technologies allowing computation to be performed at the edge of the network, on downstream data on behalf of cloud services and upstream data on behalf of IoT services. Here we define “edge” as any computing and network resources along the path between data sources and cloud data centers. For example, a smart phone is the edge between body things and cloud, a gateway in a smart home is the edge between home things and cloud, a micro data center and a cloudlet is the edge between a mobile device and cloud. The rationale of edge computing is that computing should happen at the proximity of data sources. From our point of view, edge computing is interchangeable with fog computing, but edge computing focus more toward the things side, while fog computing focus more on the infrastructure side. Edge computing could have as big an impact on our society as has the cloud computing.

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Edge AI is not killing the cloud

1. Edge AI is not killing the Cloud Rather, Edge AI heralds the coming of age for Cloud AI

2. Cloud AI now has hyper- scalable infra, computing power & powerful AI models to decipher anything But the missing link is data...

3. Data is collected on the edge Edge AI pulls the data center closer to where the data is created

4. We process data & do analytics on the edge In the new normal, we don’t ship all data to the cloud

5. This doesn’t mean Cloud AI is dead

6. Big Data Analytics is done on the cloud Edge AI only creates Event based data on the edge

7. The future of AI is not "Cloud only" or "Edge only" It will have an "Edge first, Cloud next" mentality

8. 3 Major reasons why Edge first is the Future of AI

9. The overall cost of sending video data to the cloud and processing it there is high. Edge reduces data sent and computing to be done on the cloud Why Edge AI? Cost 11

10. Latency is defined as the time difference btw. input and output Latency sensitive applications such as Accident avoidance can’t wait for sensor inputs to move to the cloud and revert Why Edge AI? Latency 22

11. Why Edge AI? Privacy The increase of audio and video sensors has put everyone on edge. With numerous instances of “switched off” devices listening to conversations processing on edge reduces privacy concerns 33

12. If edge is so good, why send data to cloud? Can’t we do all on edge?

13. Edge AI applications such as IOT & sensors collect data from a single physical point and process it. Edge AI focuses on collecting end-point based event driven data

14. In the cloud, we aggregate the event data from edge, derive insights and glean accurate forecasts

15. The future of AI is Edge-Cloud Hybrid Edge AI creates end-point based event driven data Cloud aggregates and processes the shared edge data

Edge AI is not killing the cloud

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