!(https://hackernoon.com/hn-images/1*VZ_2XyhNDygBcXk7JVdkZA.png)\n\nIt’s not a news that ML and AI serve as backbones of many systems and power unique user experiences across different business verticals.\n\nThough [machine learning and AI can be used to cut business costs](https://perceptionbox.io/blog/how-ai-can-cut-your-business-costs/) due to the automation and the ability to perform specific tasks a huge amount of time without loss of quality. However, the main reason why companies embed machine learning systems and algorithms is that such systems are aimed to improve business processes by processes efficiency growth. Today, we will walk you through the most renowned use cases of machine learning development in the modern realm.\n\n### Machine learning development at scale: Uber lesson\n\nTo begin with, Uber’s all-in-one machine learning development platform Michelangelo is among the iconic use cases of ML. In essence, the system allows engineers to create and deploy systems at scale. With its ML-as-a-Service solution, Uber addressed the challenge of bridging the gaps in standardization and tools across the whole company.\n\n!(https://hackernoon.com/hn-images/0*yhcgEBEA9q0Z0eTB)\n\nSo, prior to Michelangelo, there was no similar solution at hand that would be a catalyst for machine learning development. Before launching its proprietary solution, Uber’s engineering team was mostly using open source instruments. The transportation company taught all a lesson in machine learning development and set an industry best practice.\n\n### Dating scene: Smart looks on Tinder\n\nAs for love life**,** ML helps Tinder users with the choice of photos to upload. Smart Photos is the feature capitalizing on machine learning development. For those who don’t know what it is: the feature can be activated from profile editor and it uses machine learning development to analyze user pictures taking into account swipe left or swipe right count by showing different people swiping on Tinder a different order of profile pictures.\n\n!(https://hackernoon.com/hn-images/0*QVgGbLBJ1cOW0IOq)\n\nAfter, Tinder shows the most photos first. Such an analysis allows users to see which of their pictures are most liked by other users. Later, this machine learning development can help users make better choices when uploading profile photos. Beyond these useful insights, depending on swiping “habits”, Tinder can show users profiles that are likely to attract their attention and swipe, match, chat with ease.\n\n### Tasteful entertainment for the whole family with Nexflix\n\nMedia giant Netflix sorts its users and distinguishes the audience with the help of machine learning development. [They say](http://www.wired.co.uk/article/how-do-netflixs-algorithms-work-machine-learning-helps-to-predict-what-viewers-will-like) the majority of the shows available are found with a hint of Netflix. In other words, the company uses intelligent algorithmic approach to suggest users on the programs that would best fit their preferences.\n\n!(https://hackernoon.com/hn-images/0*OAZFHcGFvQplZCqS)\n\nNetflix harnesses the potential of machine learning development to create a user-centric experience and go beyond recommendations with a broad genre in mind. And another thing that makes them stand out that it looks into implicit and explicit user data.\n\nFirst, it analyzes what type of TV shows a user likes the most. After, with its machine learning development, Netflix looks even deeper and sees e.g. how fast that user finished 3 seasons of Homeland: if it took 48 hours with short coffee breaks in between, it means the user is likely to be a huge fan of similar series.\n\n### Shopping cart made larger\n\nNowadays nearly every major retailer being it a local chain of stores or an investment bank has a “predictive analytics” unit. The main goal of the latter is to learn all about how buyers shop, as well as sneak, peek into their personal routines in order to tap into their world and decide how to market the product or services better.\n\n!(https://hackernoon.com/hn-images/0*ovMchCiX6xIxNUUG)\n\nIf you look at machine learning development scene today, you’ll probably discover that the publisher was absolutely right. For instance, if you go on Amazon and look into any product from baby hooded towel to the best-selling book The 7 Habits of Highly Effective People, you’ll discover _Customers who bought this item also bought_ thread where a merchant’s machine learning development engine makes a suggestion and offers you similar books favored by others.\n\nTo top it off, if you become an “experienced” online shopper, the store would display a _Recommended for you from Our Brands_: here you’ll see a variety of item suggestion based on your browsing history. Finally, _Additional items to explore_ would feature products in the same category of your choice so you can extend your buy list further.\n\n### Facebook arranging your schedule\n\nEver noticed that when you talk to a friend on messenger and say that you drop your dog tomorrow, Facebook suggests adding this event to your calendar? The algorithms recognize today, tomorrow etc words and take the burden off your shoulders by using machine learning development.\n\nBasically, Facebook uses ML on all fronts. For instance, if you upload an image with a group of friends or pets or just a picturesque background, then you might get a recommendation to tag other people. Furthermore, the network utilizes machine learning development recognizing faces and suggests exactly the people appearing on that picture.\n\nThat is not all amazing and inspiring examples of machine learning usage that we know (we’ve collected a bunch of them through the years). If you want to know we’ll appreciate your claps and comments. See you soon!