Introduction

Artificial intelligence has glorified many sections of recent times and among them, one of the most notable ones is the computer vision. Computer vision-based AI can have significant contributions in the classification of specific images, object detection, and segmentation and all of that can be used directly or indirectly in consumer aspects. From face recognition in verifying user identity to digital number plate detection in vehicles, computer vision is playing a significant role and undeniably gaining much popularity.

Algorithms

Neural networks and some specific deep learning algorithms are the most important constituents of the computer vision. Among those algorithms, CNN's, GAN's, and SSD's are notable. For a newbie or even for an average skilled machine learning developer the Convolutional Neural Network (CNN) is one of the most complex sections. CNNs are not only complex to understand but also they have some specific limitations that may discourage a beginner. According to some of the beginner ML developer it's infrastructural training models are too expensive to buy. Another important reason that made it out of reach is that even after evaluating it the success rate of the deployment remains a challenging step.

How to Integrate with Hardware

In generally the AI developers try to turn the CNN models to a particular AI hardware or more specifically to an AI chip. The next step should be to integrate the application to the specific chip for a particular purpose. Some of the most renowned fabrication brands like NVIDIA, Intel, Qualcomm are the main manufacturers of AI chips or the AI accelerators.

Hardware Specifications

Microsoft and Qualcomm have worked hand to hand in the development of the Vision AI Developer kit as well as ensuring to simplify the complexity of it's the algorithm's deployment. ML developers are able to find a solution from cloud-based AI by Microsoft and Azure's IoT services to train specific models during the deployment to edge devices having a smart camera, powered by an AI chip from Qualcomm.

AI Vision Kit vs Deeplens

The Vision AI Developer kit has certain aspects that can differentiate and prove this to be more efficient than it's the closest competitor, the AWS deeplens. First of all the earlier one has Yocto Linux while the later one run on Ubuntu(OS_16.04) . In the camera section, Microsoft's one comes with 8MP(4K-UHD), while Amazon's one has only 4MP(1080P). The low energy wireless technology of Vision AI Developer is another fascinating and efficient aspect to have this one in particular purpose rather than choosing the AWS deeplens. However, the AWS deeplens lets the developer run the deep learning samples in the attached camera that can make certain analysis on anything that is visible.

Potential Applications:

The major and outstanding applications of the Vision AI developer kit is to build special-purpose applications that maybe even used to detect the faces of each employee of an office to count the individual items of the shelves whether they are available or not.

Object detection technique

This device has the ability to create an artificial vision by sending pre-tagged images to Azure storage and getting the help of the Azure custom vision service to solve the remaining complexity.

Summary

So considering the above information, the Microsoft AI Developer Kit has certain aspects that can make it more useful over AWS Deeplens. But this aspects may vary user by user. But the operating system and hardware configuration of this may be proven friendly to most of the users of this sector.

References:

1. https://www.forbes.com/sites/janakirammsv/2019/09/09/microsoft-vision-ai-developer-kit-simplifies-building-vision-based-deep-learning-projects/#4ed916a3284f

2. https://venturebeat.com/2019/09/03/microsofts-vision-ai-developer-kit-is-now-generally-available/

3. https://aws.amazon.com/deeplens/

4. https://docs.aws.amazon.com/deeplens/latest/dg/what-is-deeplens.html