Setting up AI - ML Lab with Data Engineering, Data Visualization, Big Data.


What do you think if you have a lab of this kind in your university, where students can learn to implement AI-ML concepts and use Middleware, Data Visualization, Big Data, Data Engineering and many more?

Not only that, when you invite a large global IT giant to visit your campus, do you think showcasing a lab setup of this kind will impress them and they will be keen to recruit engineers from your university? 

This kind of showcase area is present only with 2-3 selective corporates, but every IT organization know about the “Amazon Go” – cashierless checkout concept.

What needs to be done to get this enabled? The university needs to set up the physical lab with a set of equipment, which includes Face Detection cameras, Raspberry Pi devices,  weighing machines, racks, some merchandise products to keep on the racks etc. All these will be part of a one-time investment. 

Then it comes to setting up the IT infrastructure. This involves cloud space, and core software procurement (most of them open-source and no cost involved).

Finally, the integration and implementation. Here we will be using a few built-in APIs which will enable the students to use the “low-code/no-code” concept and some areas where they need to do the coding. Students of every batch will learn this part from scratch.

Reading so far, you may think that the students who are specializing in AI-ML can use this lab! But think about the real-life scenario. Where a store will have a 100 customer footfall every day, you need to use BigData, Data Engineering, and Data Visualization to make the entire setup meaningful. We cannot forget about the usage of middleware which will integrate different modules. In simple words, every student of your Computer Science Engineering Division can use this lab, and learn technologies with real-life implementation.