So with the end of the Project at Infineon Technologies, Bangalore which was a Research Project for the company, and my third and fourth semester of the MTech program, I would be moving from Semiconductor Industry to Healthcare Industry with Optum, UHG and I am greatly excited to transition into something impactful and backend engineering.
With my little experience in the industry so far and what I heard from peers the organizations that are not 100% software product companies trying out various combinations of including machine learning in the existing line of products or use cases, so the organizations start with various projects spread across multiple teams but the only handful of them make it to a presentation room where their business value comes into existence rest are sometimes just sent to archives as a case study for what didn’t work or couldn’t work right now but will be viable few years ahead due to several reasons, this is valid enough reason not every problem or machine learning solution works as needed. The Other side of the coin is the hired team or Individual machine learning engineer that has to find a place or move into other projects as the ML project is being scraped, and into different technologies, and this may vary from organization to organization but it is a tend saw many of graduate interns being hired for different projects but months later end up doing the different project or department then what they have been learning up to.
I don’t have much experience but both companies should hire people based on the project and individual need for the project and keep the preference and skillset in mind while assigning new projects and freshers out of college should pick the projects and work they like doing and see a future, rather than chasing the CTC numbers or brand name and then not liking their daily job.
Since the start of the master’s program I have been spiraling down the machine learning and deep learning track from, college projects to word autocomplete using machine learning at the hackathon to my Face recognition match library for java cards along with more than a dozen ML courses from Coursera, but with my new work won’t be doing ML instead will be into pure backend engineering which is as cool as ML for me but the question arises what to do with the acquired ML knowledge, keep it on hold with regular revisions and hands-on for side projects and learning or just let it sit until I find some use case to fit ML into, well anything learned is never goes to waste and just broaden the scope of understanding, so one more feather of ML in the hat of jack of all master of none yet! Side Project Update (Solvepao.com) With complete new full-stack frameworks and a lot more things and skillset, I am learning at the workplace will be using the same for the side project instead of writing it in (C#, Blazor using web assembly ) as I started on it earlier so more delay on that but will at least put up a nice landing page for the domain, I purchased which has been just sitting there with ever-increasing countdown timer, so will use the project as a test bench to implement the new learning.
Have a few side quests and learning projects and challenges, stacked up for me but will put them on hold as with my ongoing training in the company have already tons of new things to learn. Let’s see what’s next, will try to write more for sure Happy Learning :)