Well, I'm not sure my career is the kind of non-typical, because I started to the software engineer after graduating from Princeton bachelor's degree in computer science. I was working as a software engineer for 10 years before I decided to pursue graduate studies at UC Davis. And I did my master's part-time before that. But at UC Davis, I was primarily focused on the visualization of data sets for scientific organization and, rendering. And while that was happening, that's when machine learning became really kind of, dominant and very popular field. There are lots of opportunities in Machine learning. I saw with my visualization projects kind of led me to the machine learning. While pursuing my PG, I switched the industry and, I start working first for company called Ancestry that comes in San Francisco. That was that. Maybe the company had lots of data but wasn't really sure how to use machine learning to actually analyze that day. I was brought initially as a visualization kind of scientist. But then I got involved in Machine Learning and I was actually one of the first people there who helped them with their relevance ranking overwritten for the search record search. Right now, that's basically using milder form a line and, after that, I kind of switched completely to machine learning. And then I left Ancestry after five years and joined Apple, the fraud detection team. And again, it was kind of similar in the sense that Apple has got lots of data, but they were growing their machine learning expertise and especially the field of fraud detection machine learning. That's where I am today.

Apple is basically working from the office. Apple doesn't like people to work from home. Sometimes you have to travel for work, but most of the time we're basically working from the offices. But the offices are located in multiple geographical regions. Well, I'm a kind of Sr. machine learning engineer. So as much of an engineer at Apple, you're expected to basically do the analytics, train the model and also deploy the model. Basically, responsible for the whole life cycle of the model. And you're also responsible for making sure the model works after you deploy. So machine learning piece which is a kind of smaller piece, but then the implementation piece production, implementation, and then you also need to monitor your model is in production.

I think very much depends on the project. I mean, right now, it's not a secret that everybody's using Spark and Hadoop is being used for quite some time now. It all depends on the amount of data. Sometimes you come and join companies that only use relational databases. And they have to figure out the way to transition from that kind of extracting data from whatever source you have into the platform that will help you solve your problem. It's really about figuring out what tools, what platform you need to get where you wanna go, So that's a first. We'll have to figure out what you want to do then you can make better-informed decisions. But sometimes technologies like rational databases can still be important because they address certain business needs. You can't just replace them with Hadoop because, for instance, it would not be optimized for realtime kind of transactions. So it all depends on the platform you want to use.