This solution pattern showcases an architecture which is scalable and efficient system capturing and responding to streaming data using Kafka as the streaming platform and AIML. With Event-Driven Architecture this system can connect to, and consume from a number of systems, services and data sources by responding to triggering events.
This architecture demonstrates how an Event-Driven Architecture with Red Hat AMQ Streams and OpenShift Serverless can help build an intelligent system with OpenShift Data Science platform to drive business insights and drive an event-driven workflow.
Common use cases that can be address with this architecture are:
- Machine Learning and Real-Time Analytics to build business intelligence
- Fraud Detection in financial institutions
- Personalized Recommendations
- Forecasting demand
- Image and Video Analysis for object detection, monitoring, face recognition
- Sentiment analysis
This solution pattern extends Globex a fictitious retail store which has undergone modernization journey and has already adopted Kafka as a streaming platform. Globex wants to now extend their eCommerce website to allow customers to leave their reviews of the product catalogue.
Globex would like to
- Moderate the language comments to ensure foul language is appropriately filtered out
- Build a Sentiment Analysis system to gain business intelligence based on the product reviews
The Solution Pattern page can be found at: https://redhat-solution-patterns.github.io/solution-pattern-sentiment-analysis
