DBTB INT Shourabh Rawat

yeah so I'm sorry bravas I work for yeah so I'm sorry bravas I work for trulia zillow group so I think data by trulia zillow group so I think data by the way is a nice conference in done the way is a nice conference in done this sort of covers a diverse set of this sort of covers a diverse set of topics and that allows me as speaker to topics and that allows me as speaker to sort of sort of present to a wider a sort of sort of present to a wider a wider range of audience and be getting wider range of audience and be getting to know like how usually like working on to know like how usually like working on as a data scientist on a particular as a data scientist on a particular domain yourself do not see different domain yourself do not see different perspectives that you people have two perspectives that you people have two were the same problem on the same kind were the same problem on the same kind of data this speaking here allows you to of data this speaking here allows you to sort of interact with those those people sort of interact with those those people and sort of get their insides get their and sort of get their insides get their problems and get a better understanding so I think data is the reason why the so I think data is the reason why the school is because the data has there's a school is because the data has there's a lot of data so first of all and to lot of data so first of all and to convert that data into information is is convert that data into information is is a major ingredient that is that is a major ingredient that is that is required to sort of make any business required to sort of make any business profitable or right since ever now profitable or right since ever now nowadays like everything is going nowadays like everything is going everything is online right to really get everything is online right to really get ahead of the game you need to understand ahead of the game you need to understand your customers better your users better your customers better your users better and each and every inch of their and each and every inch of their activity and so the day is sort of activity and so the day is sort of converting that data which is sort of converting that data which is sort of rich in like they say the query logs or rich in like they say the query logs or in terms of what they are interacting in terms of what they are interacting with is in is key key to sort of with is in is key key to sort of figuring out how you can multi monetize figuring out how you can multi monetize your business so the the main idea behind my talk was so the the main idea behind my talk was too like was to figure out like how to too like was to figure out like how to deploy image recognition systems at when deploy image recognition systems at when you have a really small team so mostly you have a really small team so mostly especially in Atkins scenario where we especially in Atkins scenario where we have a data science team that wants to have a data science team that wants to sort of provide image recognition as a sort of provide image recognition as a service right and usually what happens service right and usually what happens is in standard environments usually have is in standard environments usually have data scientists working separately from data scientists working separately from from the actual deployment architecture from the actual deployment architecture as a result what happens is that you as a result what happens is that you will basically have batch processes and will basically have batch processes and and iterations are much slower because and iterations are much slower because then you have the data size have to then you have the data size have to explain their models to the to the explain their models to the to the deployment engineers to sort of sort of deployment engineers to sort of sort of make that deployable with this job we make that deployable with this job we said like image recognition is an ideal said like image recognition is an ideal candidate where we can actually use an candidate where we can actually use an existing Python web stack and allow data existing Python web stack and allow data scientist to seamlessly are easily sort scientist to seamlessly are easily sort of use that to allow for a sort of a of use that to allow for a sort of a near real-time sort of prediction engine near real-time sort of prediction engine and then provide that as a service to and then provide that as a service to across across the company to sort of across across the company to sort of sort of conform to a like a sort of conform to a like a service-oriented architecture where data service-oriented architecture where data science or image recognition becomes a science or image recognition becomes a service so Java talk was all around like service so Java talk was all around like how you can easily use like famous like how you can easily use like famous like celery and Django and build such celery and Django and build such deployment framework easily on top of deployment framework easily on top of your existing image ignition libraries so to become a good data scientist I so to become a good data scientist I think there should be we should have at think there should be we should have at the curiosity sort of two kno know about the curiosity sort of two kno know about data so have to explore to explore new data so have to explore to explore new new articles new blogs and read about it new articles new blogs and read about it understand it and being able to understand it and being able to continuously experiment and try and sort continuously experiment and try and sort of a trial and error and and sort of of a trial and error and and sort of continuously build a build a better continuously build a build a better understanding of the data and create understanding of the data and create models that provide value value to us to models that provide value value to us to your model to your company and to the your model to your company and to the users