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· 4 min read

Overview​

As per a survey, 57% of young individuals agreed they do not have the right connections to find a mentor and more than 50% of them couldn't find a job that they are passionate about. As a result I was exploring if there is any platform that would solve this major problem. Yes, there are some existing online apps but those don't serve the complete purpose to the extent that i expected. I decided to start a pet project to build this platform during my spare time , in this post i will be sharing the architecture of the application and how i was able to quickly spin up this application.

 Prerequisites to build and deploy​

As an Azure fan, i have used Azure as the cloud platform to deploy this solution. Here are some prerequisites first:

  • An Azure account (don’t worry, deploying this app costed me almost nothing, you can use free trial)
  • node and npm (preferably the latest versions)
  • VSCode and Android Studio

As i explained in the previous posts, Cosmosdb and Azure Functions are great combo to build applications and deploy in quick time without worrying about underlying infrastructure. You can read about some of the reference architectures i have posted in the past from the below links,

Let me dive into each component in the architecture of 'MentorLab'.

How it works?​

MentorLab has been made to scale up the existing students and mentors using Azure Services and Serverless Architecture to provide a cost-economic one stop solution which is dependable and truly secure. The objective is to give the students a platform which is built on a serverless architecture and can be remotely accessed irrespective of geographic location.

There are two facets of this solution​

  • Mentor Side - Dashboard (Flutter)
  • Students Side - Mobile App (Flutter)

Azure Services Used ​

  • Github Actions
  • Active Directory (A2D)
  • Blob Storage
  • Azure Cosmos DB
  • API Management
  • Azure Functions
  • Monitor

Architecture of MentorMe App

Flutter App is the front end application which is accessed by Mentor and Developer with different types of logins, All the requests from the mobile app will be router via the AppGateway. The backend APIs are built as servelress APIs with Azure functions with the support of Cosmosdb Trigger. Cosmosd's serverless feature is a great offering when building these kind of applications, as it is a cost-effective option for databases with sporadic traffic patterns and modest bursts. It eliminates the concept of provisioned throughput and instead charges you for the RUs your database operations consume. In this scenario, i have chosen Mongo API for the CRUD operations. The APIs are registered as endpoints with the Azure API management with right policies in place.

Some of the additional components you could see in the diagram are the CI/CD pipelines with Github Actions and Azure AD B2C for the authorization, Key vault for storing the connection strings,keys in a secured way. And finally application insights to generate the related metrics and for troubleshooting.

It nearly took just 3 days to build this application and going forward i am planning to add more features such as video conferencing with the help of Azure Communication and Media services . All these components just costs 36$/Month to host this application on Azure.

Hope this reference architecture helps you to kickstart your work for similar application. Feel free to add your thoughts/Questions as comments in the section below. Happy Hacking!

· 2 min read

Biometric Face Recognition is the process and ability of a bio metric machine to identify and recognize the face of an individual. It deals with either to grant access to a secured system or to find out the details of a person by matching the face with existing data in the machine’s system.

Facial recognition is full of potential and can be easily incorporated to increase the security measures of any device/object. Apart from all the excitement, this technology is still developing, the more faces are fed in the algorithm, the more accurate it becomes. Therefore, there’s no need to be afraid of facial technology as it is being used for good ethical uses and safe practices.

Industries around the globe have already started to use face detection for several purposes. As a series of my ideas, continuation with Architecture for traffic problem.

This post focuses on one of the solution with Azure by using various services such as Cognitive Service, Blob Storage, Event Grid, Azure Functions and Cosmosdb to build the right architecture that would solve the above mentioned use case.

Overall Architecture:​

Components used:​

  • Azure CosmosDB
  • Azure Functions
  • EventGrid
  • Cognitive Face API
  • Storage (Queue,Blob)

Architecture:​

The above architecture is very self explanatory, it comprises of two main components/flow.

  • Training Faces of Individuals
  • Identifying faces of Individuals

Every process is achieved in the above case using Azure function. Each operation will have separate function to achieve result. Main operations such as RegisterUser,TrainFace,TriggerTrain are simple Azure functions in the above diagram. Images are uploaded to Blob Storage using SAS token and face detection is done using cognitive service and references are stored in CosmosDB. EventGrid is used to route the events according to the invokes, for ex, to train image whenever a user uploads while registration and tag the face of the individual in the database.

I have used the above architecture with one case study and hope it will help someone out there who wants to build similar solution. Cheers!