Companies that got benefitted from AWS

  • AWS is a subsidiary of Amazon which basically offers reliable, scalable, and inexpensive cloud computing services to its users.
  • The most liked idea that boosted AWS’s popularity when it was started on 2006 was its “Pay as we go” model which states that you only have to pay for what you use. Eg. Computing power such as RAM/CPU or Storage such as Hard Disk and that too at reasonable and affordable prices.
  • This prompted millions of customers, including the fastest-growing startups, largest corporations, and top government agencies, to turn to AWS to cut costs, improve agility, and accelerate innovation.
  • Following its reputation as the “Creator of the New Era,” it began grooming itself with a plethora of services, which is why AWS is the world’s largest Cloud Computing service provider, offering over 2500 services and accounting for more than half of the global Cloud Computing industry.

COINBASE (Using AI To Fight Fraud)

  • Coinbase is a safe and secure platform for buying, selling, and storing cryptocurrencies such as Bitcoin, Ethereum, and others.
  • Since the present years, the entire market has grown around the trading of digital currencies, with investors and speculators keeping close watches on every movement, AI is a catalyst for secure cryptocurrency exchange at Coinbase, and it is one of the top users of AWS machine learning technologies.
Digital cryptocurrency(Bitcoin)
  • Coinbase, like all financial services organizations, must deliver a consistent experience for customers while also ensuring that the environment in which they operate is secure. For this, the company uses Amazon Web Services’ machine learning technologies and artificial intelligence (AI) (AWS).
  • 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, using Amazon SageMaker, a tool for easily building, training, and deploying machine learning models.
  • To combat scammers, Coinbase utilizes SageMaker to construct machine learning algorithms for picture analysis. A face similarity algorithm, for example, pulls faces from IDs that have been posted and compares them to all of the faces from other IDs that have been uploaded. Scammers frequently use the same photo for many IDs to avoid having to modify the face in multiple places on the ID. The company can rapidly detect forgery using this facial similarity technology.
  • Machine learning is made more difficult by restricted data access in a highly secure setting. Coinbase circumvents this problem by enabling machine learning engineers access to data logs only through code that has been thoroughly evaluated and committed to Amazon Elastic Container Registry — machine learning engineers cannot get into production servers and run code that has not been vetted.

“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.’

KIA (Keeping Customers Safe When Behind the Wheel)

  • Kia Motors Corporation, commonly known as Kia Motors is a South Korean multinational automotive manufacturer and is South Korea’s second-largest automobile manufacturer following the Hyundai Motor Company, with sales of over 2.8 million vehicles in 2019.
  • Artificial intelligence is revolutionizing the automotive industry, both on and off the assembly line. Fully driverless vehicles are becoming a possibility, while semi-autonomous technology that complements rather than replaces drivers are currently in use in automobiles. So, with the help of Amazon Rekognition, Kia uses AI to personalize driver experiences and improve safety.
AI Powered Transmission Control System
  • Blind-spot detection to autonomous emergency braking is all examples of AI-assisted driver aid. Kia is integrating computer vision within the cabin to better comprehend and assist drivers, in addition to external-facing sensors that assist with autonomous braking and lane departure warnings. Kia can offer driver-assistance features like tailored mirror and seat placement for different people behind the wheel thanks to AI technology like Amazon Rekognition for enhanced image and video processing.
  • For Kia, Amazon Web Services(AWS) AI services for computer vision are directly integrated into the in-car system. Image and video data about the driver, such as changes in emotion, such as if the driver is smiling or appears fatigued, as well as ambient data, are evaluated in real-time with Amazon Rekognition.

“Kia’s goal is to use AI technology to enable in-car DUI detection, as well as to monitor driver fatigue, in order to help reduce fatality rates. So with the help of AWS service called Rekognition KIA leads the world in terms of AI powered cars and all of their AI work depends upon the AWS”

MOBILE PREMIER LEAGUE(A Fast-Growing Mobile Gaming Market)

  • MPL is one of the biggest examples that went from zero to 40 million users on AWS.
  • Mobile Premier League is a mobile e-Sports platform based in India.
  • The Mobile Premier League (MPL), based in Bangalore, is one of the largest and fastest-growing players in the eSports industry, with more than 40 titles available on its eSports platform. All games can be played for cash prizes, including fantasy sports and the country’s favorite, Rummy. The MPL mobile app was introduced in September 2018 and within three months had accumulated 10 million users, exceeding the company’s one-year subscriber goal.
Play all types of games in MPL
  • Because several of the startup’s DevOps engineers had prior expertise with Amazon Web Services (AWS), the company launched on the platform, which sped up time to market. MPL’s AWS Cloud infrastructure prioritized scalability and automation as well. Amazon Aurora was chosen as the company’s principal database, and Amazon Relational Database Service (Amazon RDS) was used to handle administrative duties including provisioning and backups. However, as MPL’s dataset increased in size — particularly in terms of unstructured data — it discovered that the Amazon DynamoDB NoSQL database service, which provided low-latency data access and easy horizontal scaling, was preferable for gaming use cases.
  • For the data-heavy workloads typical of gaming firms, Amazon DynamoDB can easily handle volume, velocity, and veracity. Furthermore, at peak periods such as national sporting events, when online traffic for MPL’s fantasy games can reach 2.5 million hits per minute, the database automatically scales capacity to maintain performance. MPL’s architecture and resource planning rely heavily on automation.
  • Its DevOps engineers employ a microservices architecture for the development and use AWS CodeDeploy and AWS Lambda to automate the deployment of more than 50 different microservices.
  • AWS solution architects met with MPL multiple times to explore the advantages of containerization. Following that, MPL did its own analysis and decided to use Kubernetes to containerize its microservices. The goal is to improve operational efficiency while also stabilizing application performance at scale. Its engineers run the containers with high availability using Amazon Elastic Kubernetes Service (Amazon EKS).

In its road to expansion, MPL launched its gaming platform in Indonesia in July 2019 and is awaiting the launch of AWS data centers in the country by 2022. Aphale concludes, “AWS has been by our side throughout our entire growth journey, from debugging to stabilizing and optimizing to now expanding our product.”



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