Factors driving commercial deployment in Edge AI and Computer Vision

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Computer vision and Edge AI are witnessing a booming high-pitch in AI-based applications and services. To highlight even better, we see commercial potential in utilizing artificial intelligence techniques; the usage of relevant information includes data security, privacy, and potential bias issues. This podcast will give you a deep insight into the factors driving commercial deployment in Edge AI and Computer Vision.
The questions this podcast will answer:

01

What are four crucial factors that drive computer vision movement from the pursuit of research to commercial deployments?

02

How are the datasets collected to train the machine learning algorithms?

03

List out five significant factors that explain why computer vision is being deployed on the Edge instead of the cloud?

04

What are the common challenges in deploying commercially practical Edge-AI applications?
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Podcast Transcript:
Computer vision or visual AI deployment at the Edge is accelerating rapidly the adoption of AI-based computer vision is fueled by rapid improvements in deep learning processors sensors tools, and platforms, with VisAI labs being an early player in this market. over the last few years, we have seen the following factors drive the movement of computer vision from the realms of academia to commercial deployments; some of these factors include machine learning and or deep learning;

Over the last few years, we have seen a maturity in the deployment of DL and ML frameworks, which has allowed us to do almost real-time AI for computer vision algorithms with the DL frameworks like TensorFlow, Keras, by dodge and cafe, the market product builders no longer need to focus on creating their own frameworks or neural type of topology they just have to identify the best algorithm and tune it to that application.

The second reason for the increasing adoption of edge-based computer vision is the availability of large volumes of data.
Over the last 20 years since the advent of the internet, we have been collecting data in the form of text images and videos.

Over the last half a decade, the processing part has also become large enough to help us structure this data and make sense out of it.

This easy availability of data has allowed us to create powerful ml models that are currently trained for the most basic of use cases alongside algorithm implementation optimization and software integration with hardware data set curation and annotation is also one of the biggest challenge areas in creating a computer vision solution that product developers can easily use.

The third factor which is driving the fast adoption of computer vision is the advent of relatively low cost and, most importantly, energy-efficient hardware be the market leaders like Nvidia jetson Xilinx intel or even google the proliferation of powerful deployable edge devices have made it easier to build computer vision solutions on Edge.

Finally, one of the most important factors that drove edge adoption is the proliferation of cloud. Yes, the increase in cloud adoption has actually led to the proliferation of Edge. This so-called hybrid model envisions draining the ml model on the crowd and pushing the trained model onto the Edge.

In addition, the cloud also helps in the management of hundreds if not thousands of these deployed Edge devices. The combination of these four factors is one of the main reasons why computer vision has become deployable in the Edge. It also makes economic and social sense to move to the Edge.

The five major so-called BLERP factors which I keep talking about include:
a bandwidth that is it’s really difficult to ship a 4k video from 50 cameras to the cloud to do computation every single second.

Secondly, latency, most ai based applications require real-time insights for fast decision-making usage of cloud as processing an inference engine just takes up way too much time. Thirdly its economics’s far cheaper to have an Edge device rather than a combination of a cloud plus Edge inference engine.

The fourth most important getting inference done within the device is far more reliable than, let’s say, depending on multiple factors such as network cloud, etc., to get your processing done.

Finally, privacy people just do not like it when we record videos of them and their belongings and send it across the web also laws and regulations are slowly prohibiting this as well it is just easier to process the videos on the Edge and just send the metadata across to the cloud for analytics.

So computer mission is all the rage right now, but then there are still many challenges in making computer vision ready for the commercial the main problems include the right data choosing the right processor or camera because of the previously chat there’s an overwhelming range of choice in confidence as well as you have understanding the right methodology to build these robust solutions that are as an edge product development specialist.

We have to understand it’s not just about building a train to DLNS anymore.

Also, all this said and done, one of the main problems computer vision on edge product development specialists have to avoid is the reinvention of the view and making sure the products are built and deployed in the given time adversary labs as trained AI and product development specialists

we understand the major moving parts within Edge services and help enterprises incorporate edge-based computer vision solutions into their products faster.
Thereby decreasing the time and the risk taken to go to market.

We have done this by building a repository of pre-built tunable algorithms around people vehicle and face deduction and tracking in addition to expertise in Nvidia deep stream SDK.

Well, most firms have worked with had a problem either in hardware selection that is choosing the right processor or camera or had problems in software development and integration during the product development phase in software, the problems ranged from optimizing the algorithms for a particular processor putting the AI models to the Edge.

In addition we also faced a lot of problems in training the ai model and improving accuracy as most of the product developers who are working on solving this problem are themselves in the RFD phase with someone who understands this saves them cost and time.

So the best way forward to test your hypothesis is feature viability platform feasibility etc. is by going through a fast four to six week AI cycle this is exactly where VisAI labs can help you with will help many customers to reduce their AI development time by using our ready to deploy AI kits, which have a combination of right fit processor camera and the requisite algorithms tunable to your specific use case.

Let’s understand this the golden age of computer vision and AI is now.

We are at the endpoint of a culmination of more than half a century of research and longing. We say labs can help you make your lead connect with us to understand more about AI and how we can help you incorporate edge-based computer vision into your products.

We are reachable either at www.visailabs.com or sales@visailabs.com
So guys, thank you for tuning in into yet another episode of our podcast unravelling computer vision and Edge AI for the real world. This is Alphonse signing off ciao!

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