What things to consider while deciding

Purvanshi Mehta

Choosing careers in itself is difficult. Add the time commitment of 5–6 years for a PhD as compared to the lucrative industry job and your mind boggles with the possibilities. I was in a similar position after my undergrad and master’s (yes both times).

If you know my career path I was luckily introduced to research during my sophomore summer where I worked on semi supervised relation extraction. Later I worked full time at a research lab before joining grad school. This gave me a glimpse of the life as a full time researcher / PhD student.

But despite the research experience I joined Microsoft (where I am currently working) after my Master’s. The process of decision making was tedious and therefore I finally pen down my thoughts.

One thing I have realized after working full time both in academia and industry is the fact that

Industry or academia you get to work on exciting stuff and are a part of the AI revolution. Now it’s just a matter of which level of the advancement you want to work on.

You want to work on porting an age old rule based system to ML/DL models? Or scale the existing ML regression model to something more state of the art? Or you could work on more fundamental problems and try to answer questions which would help in building systems not immediately but in the next 5–10 years(obviously depending on the application). I tried to answer the part of which ‘advancement level’ I want to work on by asking the following questions to myself~

How important is a research problem to you?

Industrial research is mostly product oriented

unless you are in a pure research group (which is difficult to get into just after a Master’s or even after a PhD). Usually you have a problem statement related to the product and you try to find related work about how to solve it. If you find something novel in the process, you might go ahead and publish it.

Most groups in the industry do not consider published work in their performance review process. So basically you have to be internally motivated yourself.

If you are not fixated on the problem statement then industry research groups might be a good option.

For me I found a group which was actively publishing and also had majority of people with PhDs in it. Therefore I found it a good fit for myself.

What impact means to you?

ML research is interesting but not all research is impactful. Let’s look at the numbers ~

The number of AI-related…

Continue reading: https://towardsdatascience.com/industry-vs-academia-in-machine-learning-3e304033d3f5?source=rss—-7f60cf5620c9—4

Source: towardsdatascience.com