Understanding the basics of AI at Edge

AI has gained interest in a lot of people today by the advance features it provides.

The edge in simpler terms means local processing and is often used where low latency is required.

Low latency describes Computer Network that is optimized to process a very high volume of messages with minimal delay. These networks provide real time access to rapidly changing data.

AI at edge is important because of:

1. AI at edge creates network impacts

2. It helps in Latency consideration

3. It is more secure compared to cloud. Sharing personal data at cloud is not safe as compared to sharing at edge.

4. We can optimize the data for local inference

APPLICATIONS OF AI AT EDGE

1. It helps in Self- Driving Cars

2. It can be used in personal fitness tracker watch

3. It is used in robot doing surgery

4. It is even observed in tools like Alexa and Google home

We can build various Edge application such as People Counter App.

The logistics behind this app is:

1. Convert the Model to IR format

2. Use it With Inference Engine

3. Process the output to gather relevant statistics

4. Send the statistics to Server

5. Analyze the performance

6. Analyze further use case for Server

The Edge application would be build accordingly by the using this technique.