There are multiple ways to learn data science, machine learning and deep learning concepts. You can watch videos, read articles, enroll in courses, attend meetups, among other things. But there’s one thing that there’s no substitute for.
Experience.
I have personally learned a LOT from interacting with data science experts and industry thought leaders. Their experience in managing end-to-end machine learning and deep learning projects, their thinking when building a data science team from scratch, how they managed tough projects and overcame hurdles, etc. – we simply cannot learn all of these in any course.
So, I am thrilled to present an exclusive interview with one such data science expert and industry thought leader – Dr. Sunil Kumar Vuppala! He is the Director – Data Science, Ericsson GAIA (Global AI Accelerator), Bangalore, and brings a wealth of industry and research experience.
What I really liked about Dr. Sunil in this interview was his to-the-point answers. He cuts to the chase quickly and shares his rich experience and valuable advice for our community. You will learn a lot from his answers here, regardless of the data science role you’re in or are aiming for.
Dr. Sunil has had an illustrious academic and industry career. He started off as an Application Engineer at Oracle and then occupied various research roles in AI at Infosys. He was also the Principal Scientist at Philips before his current position. Not only this, but Dr. Vuppala has also completed his M.Tech from IIT Roorkee before getting his Ph.D. in ‘Optimization under uncertainty for Energy Management in Smart Grid’ from IIIT Bangalore. He is also the visiting faculty in India’s top institutes teaching AI and ML.
Enjoy the discussion and make sure you leave your thoughts and comments below this article!
Dr. Sunil Vuppala: Being in the research stream, it was a smooth transition for me.
“I really bet on IoT 12 years ago. I realized that unless analytics and AI support the analysis of data captured from IoT, the cycle will not be complete. That motivated me to venture into the data science field.”
The organizational changes at Infosys gave me an opportunity to work in Automation and AI way back in 2012-13. Furthermore, my learnings in Ph.D. helped me to achieve the transitions. Andrew Ng inspired me in democratizing AI and serving the society with our technical contributions.
Dr. Sunil: Good question – and this is something I have seen a lot of folks struggle with. My experience has been slightly different from what you might expect.
“Since I was a part of the research wing at both Infosys and Philips, there was mutual learning involved.”
At IIIT, I was dealing with solving millions of variables while at Infosys, I was deploying the implementations of those results as a testbed. When this further developed into an applied research problem for my Ph.D., the challenge was to translate it into benefits for my organization. I needed to balance both – my academic research which prioritized publishing papers and my industry role, which focused more on patents.
Dr. Sunil: Research is a core part of any technology company and machine learning is not an exception. The focus of companies in machine learning research can be across multiple dimensions:
Dr. Sunil: Interesting question. Smart energy management is much more than optimization. Machine learning in the smart grid can be applied to:
Dr. Sunil: The most challenging project for me was when I represented my platform team for a large manufacturing client in the USA. The client was an initial customer of our Automation and AI platform. I was informed by the VP of products that he will share with me Tera Bytes (TB) of data and I need to find out the million dollars’ worth use cases for Automation and AI in his organization.
After a couple of rounds of discussions, we were able to come to an agreement that expecting magic after dumping the entire TBs of data into the platform was not the solution. Instead, the idea was to proceed by taking incremental steps. We started with 2 out of 55 applications of the client and identified the potential use cases of automation and AI within 2 days and were able to present the same client CIO. Those were the initial days of AI practical implementation.
“Now, AI is at the peak of inflated expectations and people think that AI can solve all their problems. We should formulate realistic business problems and convert them into data science problems and then work on what kind of methods will be needed to solve such problems.”
The most recent challenging project for me is at Ericsson. We are trying to predict the type of customer complaints about the telecom operators proactively and do the corrective action of configuration changes.
Dr. Sunil: I agree that the field is fast changing. I am betting more on deep reinforcement learning and killer applications in unsupervised learning including GANs in the future. We have seen tremendous applications of deep learning architectures across the domains.
However, most of the problems we are solving in the industry are of supervised learning. In the real world, the data available is not annotated.
“If we can extend the usage of Deep Learning directly with the data without the requirement of annotations, then the potential of this field is unlimited.”
Dr. Sunil:
It is important for software engineers to understand the difference between the deterministic Software Development Life Cycle (SDLC) and the vague, probabilistic Data Science Life Cycle(DSLC).
Successful data scientists are strong in mathematics, programming and domain knowledge. A software engineer can contribute to the programming aspect of Machine Learning models, their evaluation and visualization.
Therefore, software developers should identify their core strengths and choose where they can excel in this field. If they have a Computer Science background, they should concentrate on basic statistics for data scientist profiles. If they have data handling experience, they should aim for data engineering profiles.
Dr. Sunil: I strongly believe that students should at least target two projects (one during the course work as a capstone project and another in their own domain) before looking out for job opportunities.
The current job market is very good. The industry is desperately looking for bright data scientists and data engineers.
Here are a few project ideas one can target based on publicly available datasets :
Dr. Sunil: Students should aim to have a strong foundation. For this, students need to:
Industry data, on the other hand, needs a lot of preprocessing and exploratory data analysis skills. Generally, such processes do take up a bulk of our time at the industry level.
“In short, Data Science enthusiasts should participate in hackathons and maintain strong Github profiles to enhance their learning.”
Dr. Sunil: There are a lot of papers to mention!
“I strongly recommend aspiring/experienced data scientists to go through the seminal work done by the 2018 Turing award winners – Geoffrey Hinton, Yann LeCun and Yoshua Bengio.”
They are excellent professors and are now supporting the tech giants in Silicon Valley to democratize AI. Here are some of my favorite research papers in this domain:
Core-Level Papers:
Advanced Papers:
I learned a lot from the answers given by Dr. Vuppala. Coming from a software engineering background myself, his practical suggestions and insights on the Data Science industry are extremely beneficial to data science professionals.
Here are a couple of key takeaways from the interview which resonated with me:
If you have any questions or feedback or more points of view to discuss, please share your thoughts in the comments section below.
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Great! Very good article, since I am started on the data science field this is a very helpful starting point. Many thanks.
The Interview is an eye opener and a motivation to me as I try to change career to become a Data Scientist. I really learned a lot from the interview
It was a very good learning experience from one of the best industry expertise as a fresh Student in Data Scientist and Business Analytics student. I will continue read his information to strengthen me in this area of study to come out with flying colours.
Thank you for greats insights.
A very interesting read! I share your takeaways. Another thing that got me concerned in this interview, he said 'Successful data scientists are strong in mathematics, programming and domain knowledge'. I am not strong in mathematics but i intend to be a successful data scientist!
Thanks for share your insights!
Insightful and great learning
Thank you so much Dr. Sunil, the interview session has opened up my mind to possibilities and mindset to have in studying and practicing Data Science. It is so insightful.
Good insight into the role of data scientists.
Good insights from Dr Sunil
What would be his advice to those students from completely different backgrounds aspiring data science and business analytics?
A great read! full of great insights. Will surely read more about his works in the fields of Data Science and Business Analytics. Thank you for the papers shared.
An interesting read, great insights
This interview has helped me on how to test the depth of the waters as a prospecting data scientist. It has also opened my eye to what I need to properly equip myself in preparation for the challenges ahead as a data scientist. Thank you Dr. Sunil Kumar
Great interview! shows the basics on how to go from aspiring to experienced data scientist.
Great insights! I will maintain a strong Github profile, work on one project using publicly available data sets as well as one using a private one. I hope I am good enough in mathematics. programming and domain knowledge to be a good data scientist..
It is interesting to see how one can transit from one skillset to another, his experience and knowledge are insightful. I am particularly motivated coming from an engineering background. The future is so wide.
this is a great starting point. Jack Farrell
Learning from experts and getting the direction I need is great. Thanks
It was quite informative
Really interesting interview. it helps me to think about what inspire me to take courses in data science. Thanks alot
This is a great article for someone in my space where I have been in technology for 20+ years and now looking to move into a Data Science role. I appreciate your insight.