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FinTech
Amazon SageMaker, Amazon CloudWatch, Amazon EventBridge, AWS Lambda, Amazon EKS
Enhanced performance, efficiency, and automation through ML-driven predictive scaling.
The client is a prominent financial services company operating in India, part of a major Japanese financial institution with extensive experience in the credit and financial services sector. Established to serve the Indian market, the company focuses on providing a comprehensive range of credit and financial products.
Reduced Latency
Improved Resource Utilization
Intelligent Automation
Reactive scaling increased pods only after CPU or latency spikes occurred, which caused throttling and failures during sudden workload increases. Without prediction, unexpected surges overloaded services, while static peak provisioning led to unnecessary costs. HPA and KEDA also required 5–7 minutes to stabilize, which delayed the response and increased MTTR, as manual recovery was often necessary.
• Predicts traffic, QPS, and resource usage 30–60 mins in advance.
• Automates scale-out and scale-in across compute, pods, and databases.
• Enable metrics and health visibility for proactive scaling.
• Starts scaling actions before peak demand.
• Scales microservices based on queue backlog.
Achieved 65% latency reduction, 50% better utilization, 45% faster scaling, 90% fewer incidents, and 75% ML automation
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