Customer Stories / Software & Internet
Games24x7 Accelerates Machine Learning Lifecycle with Cloud-Native Data Science Tools on AWS
Games24x7 improved data science productivity using Amazon SageMaker Studio and Amazon EMR, reducing overhead and automating ML processes for faster iterations.
3x
higher productivity
10x
faster iteration cycle
20%
increase in user retention
Reliable support
Optimizes architecture with AWS support
Overview
Games24x7 is India’s leading multigame platform, with offerings such as RummyCircle, My11Circle—India’s second-largest fantasy games platform—and U Games, a portfolio of casual games. The company leverages hyper-personalization and data science to provide superior user experiences. Games24x7 sought to modernize its machine learning (ML) pipeline using cloud-native tools.
The company is using Amazon SageMaker as a fully managed development environment, Amazon EMR as a big data platform, and AWS Step Functions with Amazon SageMaker Pipelines to orchestrate its ML pipelines. With the support of AWS, Games24x7 automated post-production tasks such as ML monitoring to increase productivity and empower its data scientists to solve more business problems, faster.
Opportunity | Solving for Bottlenecks that Delay Solution Delivery
We’ve improved the quality of outcomes from our ML models as a result of our modernization efforts on AWS, and we can manage our overall data science ecosystem more efficiently.”
Tridib Mukherjee
Vice President & Head, AI & Data Science, Games24x7
Solution | Adopting MLOps for Increased Automation and Productivity
Outcome | Accelerating Iteration while Lowering Costs of Analyses
Learn More
To learn more, visit aws.amazon.com/solutions/analytics.
About Games24x7
AWS Services Used
AWS Step Functions
AWS Step Functions is a visual workflow service that helps developers use AWS services to build distributed applications, automate processes, orchestrate microservices, and create data and machine learning (ML) pipelines.
Amazon EMR Serverless
Amazon EMR Serverless is a serverless option in Amazon EMR that makes it easy for data analysts and engineers to run open-source big data analytics frameworks without configuring, managing, and scaling clusters or servers.
Amazon SageMaker Studio
Amazon SageMaker Studio is an integrated development environment (IDE) that provides a single web-based visual interface where you can access purpose-built tools to perform all machine learning (ML) development steps, from preparing data to building, training, and deploying your ML models, improving data science team productivity by up to 10x.
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