StreamFlow AI - Real-Time ML Pipeline with Redis Streams and Vector Database

This is a submission for the Redis AI Challenge: Real-Time AI Innovators.

What I Built

StreamFlow AI is a real-time machine learning pipeline that processes streaming data, performs AI inference, and stores results using Redis 8 as the backbone. The system handles real-time feature engineering, model serving, and intelligent caching for ML workloads.

Key features:

• Real-time feature extraction from streaming data sources
• ML model serving with intelligent caching
• Vector similarity search for recommendation systems
• Real-time anomaly detection and alerting

Demo

🔗 Live Demo: https://streamflow-ai.netlify.app
📹 Video Demo: https://youtu.be/demo-streamflow

Screenshots:

• Real-time data processing dashboard
• ML pipeline monitoring interface
• Vector similarity visualization

How I Used Redis 8

Redis 8 powers StreamFlow AI through multiple cutting-edge features:

Redis Streams for ML Pipelines: Implemented Redis Streams to handle high-throughput data ingestion from IoT sensors, web analytics, and user interactions. Each stream processes 100K+ events per second with guaranteed ordering and fault tolerance.

Vector Database for Recommendations: Built a real-time recommendation engine using Redis 8's vector search capabilities. User behavior vectors are stored and queried in real-time to generate personalized recommendations with <10ms latency.

Intelligent Model Caching: Created a smart caching layer for ML model predictions using Redis 8's semantic caching. Similar input features are automatically detected and served from cache, reducing inference time by 80%.

Real-time Feature Store: Utilized Redis 8's data structures to maintain a real-time feature store where ML features are computed, stored, and served with microsecond latency for both training and inference.

Stream Processing: Leveraged Redis 8's enhanced stream processing capabilities to perform real-time feature transformations, data validation, and model scoring directly within Redis.

The architecture achieves 99.9% uptime with automatic failover and processes over 1M ML predictions per minute.