Interview with Mayank Vadsola
Mayank Vadsola
Machine Learning Engineer
AWS Certified ML Specialist, 2x AWS Certified
Ottawa, Ontario, Canada
Q1. Please share your educational and professional journey with us?
To start, I have a huge interest in science and technology since high school. As part of which I started my bachelor’s journey in Mechanical engineering at NIT, Surat. After finishing my bachelor’s I planned to go out for my Masters. So, I came to Canada for my Master’s and specialized in High-performance computing, Machine learning and Computational Fluid Dynamics. During my masters, I was working in two AI-based startups and that’s how I got into this field. After finishing my Masters I started my journey as a Machine Learning engineer.
Q2. How do you motivate yourself every morning?
To motivate, I keep myself updated with the latest technology in the field of AI. I follow industry leaders on LinkedIn and other platforms. This helps to realize that there is much more to learn and implement. I think, ML is a self-motivating field, because every day you see new technology coming in. So, it’s really difficult to get bored.
Q3. Which Cloud Platform is best to deploy any Machine Learning Model?
All cloud platforms have their pros and cons but I’ll prefer AWS. Personally, I use AWS Lambda and AWS ECS to deploy my ML models. Most of the time I end up with AWS Lambda because it’s easy to use and saves a lot of costs.
Q4. Which Machine Learning Model have you used the maximum in your career?
There is no specific model but I work with computer vision a lot. In my present work, I deal with classification, object detection, optical character recognition and other problems. Personally, I am very excited about the advancement of Generative Adversarial Networks. I try to incorporate it to generate synthetic data and recently to improve the quality of my images.
Q5. You are AWS certified ML Specialist. How do you see AWS as a Cloud Provider?
I choose AWS because it has a huge user base and different services. They have lots of tools for AI and ML. So, if you want to build a quick application then this can be very easy and handy. Also, they provide many algorithms as part of their SageMaker platform. SageMaker also provides options for custom training using docker containers and it takes care of deployment as well with scalability. So, the possibilities are endless.
Q6. What kind of problems have you faced maximum in your ML Real World Projects?
Usually, to gather data for your particular application, especially when you work in a small company or startup, is a huge problem. The second biggest problem according to me is deployment in realtime. There are lots of parameters to consider. You need to keep a balance between cost and performance. Also, all deployment services have their pros and cons. For example, it’s tricky to deploy very large models with serverless functions.
Q7. Which one skill do you like most about yourself?
I never underestimate myself. I still remember when I started in the field of High-performance computing and machine learning, people said a Mechanical engineer cannot be good in this field. Well, I see many people from other disciplines doing great in Machine Learning. Just focus on your end goal and code. 🙂
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