Krish Naik is a hot shot in the field of data science education with over 397k subscribers for his YouTube channel. He is the co-founder of iNeuron.ai, where he dons both CIO and CMO hats.
Analytics India Magazine got in touch with Krish Naik to understand his ML Journey and his take on India’s data science landscape.
Excerpts:
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AIM: What attracted you to the field of data science?
Krish: Before starting in data science and AI, I was a software engineer moving between net programming and Java. During that time, I had never heard about AI. In one of the projects, we were implementing a module based on personalisation using net programming. The functionality was very simple where based on a login, we had to recommend various insurance products to the user.
Once the project got implemented, we got special recognition for this module, and the team called it an AI module. That was the first time when I first got to know about AI and machine learning. This excellent use case ignited a fire in me to learn about machine learning and data science. I started exploring more about it and could see how many different use cases and problems it could solve with ease. When I started working on some of the projects, I could see the overall development in me in terms of business, technical and presentation and many more things. Apart from this, I am also a huge fan of Mathematics and Stats, which motivated me to move towards data science.
So until now, I have worked in more than four companies where I have successfully implemented around 6-7 projects in the field of data science and helped them earn exceptional revenues. I have also co-founded a company named iNeuron Intelligence (ineuron.ai), where we focus on providing affordable courses on AI-related technologies and parallelly, we do AI product development.
AIM: What’s your advice for aspirants embarking on an ML journey?
Krish: If anybody wants to start their ML journey now, you need to focus on three perspectives:
- Business Problem (domain knowledge)
- Technical skills
- Outcomes and Improvements of the solution
Business problem: As a data scientist, we need to think about the business problem and what use case we are solving. Not every issue needs to be solved by using data science or machine learning. To start learning, we need to focus on understanding the domain because that is the best step from where your requirement gathering and data gathering processes will start. The more you understand the business and the domain, the better solution you will be able to create with better accuracy.
Technical skills: Technical skills involve understanding the programming language (I would suggest going with Python), understanding the entire lifecycle of data science projects, model deployment and retraining approaches.
Outcomes and improvements: This step involves building the product with better functionalities and modules, bringing more improvements in the products. These steps are always to be kept in mind since the industries will be working in this manner, and if you have this thought process, it will definitely help you.
AIM: What tools do you use?
Krish: From a language perspective, I use Python, javascript, .net., frameworks and related libraries. I also use a lot of GPU computing.
NVIDIA RAPIDS is one of the best tools for accelerated data science, especially when I want to do hyperparameter optimization. Unfortunately, CPUs are quite slow, so NVIDIA Cuda Libraries come to the rescue of NVIDIA Titan RTX. The NVIDIA GeForce RTX 3060 is the latest NVIDIA GPU I have been using.
All in all, I can say that this acceleration helps to complete my everyday data science tasks faster, and I spend more time in algorithm optimization and insights gathering rather than spending time waiting for pre-processing and model training to get completed.
AIM: What are your thoughts on India’s AI/ML landscape?
Krish: I have been working in the industry for about five years, and the Indian AI and Analytics startups continued to attract investment in 2019, receiving $762.5 million in funding, a 44% increase over the $530 million funding received in 2018. This steady growth in funding seems insignificant compared with the 368% growth from 2017 and 2018. This continued growth has attracted many people to move into this sector. This sector has also created many jobs, and many people are able to get some amazing salaries.
AIM: You have said 2021 is the year of hiring data scientists. Could you elaborate?
Krish: According to the recent survey from Gartner, around 4-5% have started incorporating AI in their day to day activities, and they also have mentioned that it’s going to increase in the upcoming five years. Apart from this, in the past six months in 2021, I have seen more than 500+ of my subscribers and students from iNeuron from different domains making a successful transition to data science. And more and more requirements are coming up in companies for these specific roles in Analytics. So this gives a hint that this year will be amazing for different data scientist roles.
