I got introduced to business analytics through one of my courses at IIM Calcutta’s PGDM curriculum and I instantly knew that I wanted to pursue a career in this area at some point. I spent the early part of my career as a business consultant but things changed a few years later when I joined a Whatsapp group of Engineering batchmates with similar interests. We discussed algorithms, reviewed papers, participated in Kaggle competitions and completed many courses like Andrew NG’s Machine Learning and later the Deep Learning course. The unique combination of mathematics, logic/programming, and domain understanding really got our interest.
What is fascinating in my current role is the sheer number of AI/ML use cases I come across from various Industries and functions. I work on creating intellectual properties using AI/ML from concept to realization, mostly inspired by real-world business problems. It is always very important to first define and articulate the business problems precisely and that reminds me of a famous quote from Albert Einstien – “The formulation of the problem is often more essential than its solution, which may be merely a matter of mathematical or experimental skill.” Solving data science problems mostly involves iterative experimentation and there are no fixed formulae to solve any problem. Solution for one use case may not work for another similar one and this is what makes this field so exciting.
We are always on our toes to keep up with the rapid developments as things here get obsolete very quickly. The cycle times required for development, prototyping, and productionizing solutions have been shrinking, thanks to the multiple ML platforms available today. Developing and deploying models has become much easier than it was a few years back. In this dynamic world of AI/ML, one word of advice to those getting started for a sustainable career is to set a target to learn something new every day.
While data along with algorithmic advancements remain the primary source of these amazing developments, sometimes the availability of the right data also becomes an impediment. Another challenge in the real world of AI and ML is the huge gap between prototypes and production-grade solutions. A large amount of effort and time must be apportioned for acquiring and curating data and for productionizing the solution. While it is fun building ML models, the other aspects are equally important. Despite some of these challenges, every day is very fulfilling for me and I am always looking forward to learning something new.
When I am not researching or working, I love to keep myself up to date on news from the world of automobiles, gadgets, and sports. I also used to be a guitarist in my IIM Calcutta college band and I try to pursue my musical interests in my free time. I mostly follow and play classic rock and blues genres of music.
Meet Somdev Goswami: Applied AI Practitioner, Product Manager, and Auto Enthusiast
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