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Introduction Edge AI
Processing data near the edge of the network, where the data is generated, instead of a centralized data-processing warehouse. Edge computing enables mobile computing and IoT technologies. It makes data and devices more affordable and connected without increasing responsiveness and reducing latency. Let’s take a deep dive into edge computing and understand how the technology works with Akira.ai's solution for business users.
Edge AI has the potential to revolutionize how AI technology is developed and used worldwide. Click to explore our, Edge AI Architecture and its Applications
Why do we need Edge AI?
Emerging technologies like deep learning and neural networks, which have revolutionary potential depending on cloud computing, hampers its runtimes increasing massive power requirements.
Forrester Research, an International Informational Technology Firm, reports that “ Latency is becoming an issue as firms try to push more data to software that runs in the cloud or the data center.” As the amount of data increases, it becomes more uneconomical to do all processing centrally.
Edge Computing acts as efficient technology that brings intelligence closer to the place where intelligence is needed and, in return, unleashes the collective power of intelligent devices. As the number of firms grows and increases, central software platforms handling the inflow of data are being pushed to the edge. The main motivations to choose Edge Computing are:
- Real-time data processing without latency or delay in the transfer of data.
- Eliminates lag time or allows smart applications to respond to data instant.
- A large amount of data is processed near sources resulting in reduced internet bandwidth.
- Eliminates costs ensuring applications to be used in remote locations.
- Processing data without putting it in the cloud adds security for sensitive data.
Explainable AI in manufacturing improves efficiency, workplace safety, and customer satisfaction by automating their tasks. Click to explore our, Explainable AI in Manufacturing Industry
Use of Edge AI in Video Surveillance System
Note: Before moving ahead, please visit the following docs. Ebook Till now, you have understood three things:
- What is Edge AI?
- Why do we need Edge?
- And what is a video surveillance system? How is it implemented?
- This section belongs to running the video surveillance system on Edge.
- Machine learning/Deep learning model: Model
- Edge Device: Raspberry Pi
- Framework to be used: Tensorflow/Tensorflow Lite
Popular Edge Devices and their Hardware specifications (For reference to select framework according to Device)
Architecture of Edge AI in Video Surveillance
The overall architecture of running any model includes the following steps:
Considering these steps, here is the solution diagram:
After getting the model ready, here are steps that will be followed:
|Image Detection Model||Image Detection Model
Data Preprocessing Feature Engineering
Collection of all the images
Labeling of the Images
Developing of the algorithm
|To be done on cloud/ machine|
|Image Matching Model||
Model Development (which can compare different Images)
Result and its Validation
Defining image descriptor
Indexing Image dataset
Defining Image similarity metric
|To be done on cloud/ machine
|Deployment of Edge||
Deploying model of Edge Model Validation on Edge Generating the results
||To be done on Edge|
|Running the models of Edge||Model Analysis (for maintain operational accuracy)
Disclaimer: In general, you should:
- Never use Raspberry Pi for training
- Deploy the model on Raspberry Pi and Run scoring/ prediction on it.
Usability of the Solution
- Discover more about Difference Between Cloud, Edge, and Fog Computing?
- Explore here about Explainable AI in Insurance Industry