The fast digital environment demands businesses to extract real-time data insights because this capability ensures their ability to stay ahead in competition. The presentation examines how artificial intelligence applications benefit from real-time analytics through microservices architecture combined with API integration to improve data processing and decision-making capabilities.
This presentation starts by defining real-time analytics basics and explaining its increasing importance for finance, healthcare and retail industries. The presentation demonstrates how organizations build agile systems through microservices architecture to handle variable data volumes efficiently. Each microservice functions autonomously which enables development teams to speed up their innovation efforts for feature deployment thus accelerating the entire analytics workflow.
APIs serve as fundamental connectors to establish smooth data interactions between microservices and outside data sources. I will explain how APIs enable organizations to collect data from various sources while maintaining data accessibility for analytical purposes.
The presentation will discuss the implementation difficulties organizations encounter when building these architectures by explaining latency problems and security concerns and microservice management complexities.
The session will provide participants with knowledge of optimal microservices and API implementation strategies for AI projects which will enable them to create real-time analytics solutions for faster and better decision making.