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People are so curious to figure out how they will look 40 years from now. That's not it, they are trying these filters to share a picture of them with friends on social media. Back in time, someone wise said, not a thing in this world is for free, if you have been offered free services, you should indeed look again and around. That's what FaceApp is, it's for free. Then how does one derive revenue out of this data collection? Advertising, Digital Advertising : There are plenty of ways by which publishers (people/companies who have lot of engaged users using their website or application) make use of consumers data to make revenue. 1. They show Ad's on their platform : One of the most easy and legit way to monetize their user base, is to show relevant ad's to the mobile app user. The app can be used to show ad's from direct advertiser(people/companies who want to acquire new users for using their services) or can be apart of various ad tech supply chain for monetization. 2. They sell GAID/IDFA/Device ID's : Other quick way to monetize on data collected, is to sell advertiser ID's across ad tech networks, this would empower the ad networks to target your device for ad placements across google display, affiliates and 3rd party apps. This can even trigger fake APK drops, malware targeting and even jeopardize your mobile phone to become a botnet for supplying fake impressions and clicks. 3. Delivering ML based face trained model to intelligence agencies, governments and other 3rd party users : While you clicked on that upload image button to have a look at what you would look like 40 years from now. You not only gave access to who you are, where you work, what do you search upon, and you got all of that tagged to an unique identifier, your face and trained for next 40 year versions of you. So next time you walk by a CCTV camera or any autonomous cameras around you, you are being judged. You are now being tagged with where you go, what you do, whom do you talk to etc etc. Remember, your privacy is your top concern. Today advertisers lose USD 100M a day to advertising fraud, financial institutions are facing a high tide of digital fraud all across the world, and intentional data thefts are benefiting only a few. With rising cyberspace, we need to constantly work towards creating safer and beneficial digital environment.

Understanding Linear Programming Linear programming (LP) is a method for engineers or data scientists to find the best outcome of a business problem i.e maximum profit, minimum cost in a linear mathematical model. Each of the LP problem consist of following components : 1. Objective Function : Purpose behind LP i.e maximize profit, minimize loss , 2. Decision Variables : These are the controllable variables that influence the objective function, 3. Constraints : These are linear restrictions on decisions variables. Case Example : XYZ Pharmaceuticals XYZ Pharmaceuticals manufacturers two types of medicine with same salt : A and B. The manufacturer wants to maximize their weekly operational profit. $1 of profit per medicine A. $1.5 of profit per medicine B. Medicine A requires 1 hour of manufacturing labor and 2 hours of packaging labor. Medicine B requires 2 hours of manufacturing labor and 1 hour of packaging labor. Each week, XYZ has only 100 hours of manufacturing labor and 100 hours of packaging labor available. Lets Build the Objective Function, Decision Variables and Constraints Let x be the of medicine A produced and y be the medicine B product in the week Objective Function : Max(z) = 1x + 1.5y Decision Variables (Subject to) 1x + 2y <= 100 (Available Manufacturing Hours) 2x + 1y <=100 ( Available Packaging Hours) Constraints : x >= 0 & y >= 0 Loading & Solving the Problem Statement in PuLP Discussing Solution We see that the optimal solution for production of Medicine A & B to return maximum profit is 33.33 units weekly for both A & B to maximize the profit up to 83.33 units. We can even plot the illustrative graph using matplotlib library in python. There are many commercial optimizer tools, but having hands-on experience with a programmatic way of doing optimization is invaluable.