Success Stories of companies that got benefitted from AWS
1. Coca Cola
The Coca-Cola Company, with more than 500 brands sold in over 207 countries, runs hundreds of marketing promotions every year. During Super Bowl XLVII, The Coca-Cola Company ran an ad that encouraged audience members to vote online for their favorite commercial ending. At the time, the company’s environment was on premises, and the massive spike of traffic to the site caused delays and a poor user experience. According to Michael Connor, digital marketing platform architect at Coca-Cola North America, this event triggered an internal push to move to the public cloud with Amazon Web Services (AWS).
In this replay from re:Invent 2014, Connor walks through how The Coca-Cola Company migrated to AWS to reduce costs and increase operational efficiencies. Moving to a DevOps model, The Coca-Cola Company leveraged AWS Elastic Beanstalk to enable its creative agencies to more efficiently deploy applications. The company also used AWS Auto Scaling to optimize performance and costs with its applications, allowing them responding better to sudden influxes of site traffic. By migrating to AWS, The Coca-Cola Company achieved 40 percent operational savings, coupled with an 80 percent reduction in IT help desk tickets due to added automation.
2. Netflix
Netflix is the world’s leading internet television network, with more than 200 million members in more than 190 countries enjoying 125 million hours of TV shows and movies each day. Netflix uses AWS for nearly all its computing and storage needs, including databases, analytics, recommendation engines, video transcoding, and more — hundreds of functions that in total use more than 100,000 server instances on AWS.
Netflix entertains the world, providing a wide variety of TV shows, movies, and documentaries to hundreds of millions of members across the globe in over 30 languages. Netflix builds diversity, inclusion, equity, and a global outlook into everything it does, and by fostering a culture of courage, empathy, and curiosity, Netflix can move faster to develop new stories and better ways of sharing them with its members around the world. Netflix relies on AWS to help it innovate with speed and consistently deliver best-in-class entertainment. AWS provides Netflix with compute, storage, and infrastructure that allow the company to scale quickly, operate securely, and meet capacity needs anywhere in the world. Moreover, Netflix, a leading content producer, has used AWS to build a studio in the cloud. This virtual studio enables Netflix to engage top artistic talent, no matter the location, and Netflix artists and partners have the freedom to collaborate without technological or geographical barriers.
Netflix expanded into content production in 2012 and is now one of the world’s leading studios. With a culture of continual innovation, the company wanted to build a visual effects (VFX) studio in the cloud to attract top VFX and animation artists worldwide and enable seamless collaboration between global teams. Using NICE DCV and Amazon EC2 G4 Instances, Netflix built remote workstations without having to choose between responsiveness and image quality. Learn how Netflix went from beta to launch in just 1 year, reducing technological and geographical barriers for artists while optimizing costs.
AWS chief information security officer Steve Schmidt sits down with Jason Chan, vice president of information security at Netflix, to talk about security strategy, building a security program, Zero Trust, and cats as a unique threat model.
3. Coinbase
Cryptocurrencies like Bitcoin have seen a blizzard of headlines over the past few years. These digital tokens share some of the qualities of hard currency and can be bought, traded and spent. In fact, an entire market has grown around the trading of digital currencies, with investors and speculators keeping close tabs on every fluctuation.
At the center is San Francisco-based Coinbase, a digital wallet and exchange platform where over 20 million merchants and consumers have traded more than $150 billion in cryptocurrencies since its founding in 2012.
Like all financial services companies, Coinbase needs to provide a seamless experience for consumers while taking steps to secure the environment in which they operate. For this, the company relies on artificial intelligence (AI) using machine learning tools from Amazon Web Services (AWS).
Using Amazon SageMaker, a tool to easily build, train and deploy machine learning models, engineers at Coinbase developed a machine learning-driven system that recognizes mismatches and anomalies in sources of user identification, allowing them to quickly take action against potential sources of fraud.
That’s not possible online, so Coinbase uses SageMaker to develop machine learning algorithms for image analysis to defeat scammers. For example, a face-similarity algorithm automatically extracts faces from IDs that are uploaded and then compares a given face with all of the faces across other IDs that have been uploaded. Scammers often use the same photo for multiple IDs, as they would otherwise have to edit the face in several places on the ID. With this face similarity algorithm, the company can quickly detect the forgery.
The insights gained from building anti-fraud algorithms also allow Coinbase to tailor experiences based on user types — a simple and intuitive way to segment retail-level investors who buy and hold, versus sophisticated pro users who trade a lot. In a recent customer segmentation exercise, a Coinbase analyst was able to simply write a clustering algorithm on a laptop and then run it through SageMaker to analyze how customers use cryptocurrencies, segmenting those who are interested exclusively in trading from those investing for the long haul.
But risk management is only one side of the story. Given its digital roots, it’s no surprise that cryptocurrency, like more traditional financial markets, goes hand in hand with a tremendous amount of data.
However, since Coinbase operates in a highly regulated environment, the company takes extra measures to ensure customer data is protected — even from its own data scientists and engineers. Any code that runs on Coinbase production servers has been code reviewed and looked at by multiple sets of people before it goes into production.
Restricted access to data in a highly secure environment makes doing machine learning that much harder. Coinbase overcomes this challenge by allowing machine learning engineers access to data logs only via code that’s been thoroughly reviewed and committed into Amazon Elastic Container Registry — machine learning engineers can’t actually log into the production servers and run code that hasn’t been reviewed.
At the end of the day, digital cryptocurrencies rely on trust for their existence. And companies like Coinbase rely on AWS to build and maintain that trust by working to constantly stay ahead of risks.
