So it all started with client's requirement to build a Minimum Viable Product (MVP). The backend of the product was written in python and involved processing of images using Computer Vision. We decided to host the backend in Azure. There were lots of advantages of having the backend hosted in cloud instead of having an on-premise setup. The thing that attracted us the most was infrastructure scaling and availability of the system. We now dont have to bother about infrastructure, power consumption, system availability, system failures, etc. We were in need of GPU machines to run the code in backend. We though of using N-Series Azure virtual machines for this purpose. We wanted to scale the machines horizontally to handle multiple requests. We booted up few more machines to handle the load. This is where Azure Load balancer came into picture. Since there were multiple instances of virtual machines, we needed someone to decide for us, which machine the request should go to. We connect...