AI Infrastructure
Real-time recommendations combine pre-computed collaborative filtering scores with live user behavior signals to suggest relevant content, products, or connections. This pipeline merges offline model outputs (computed in batch) with online features (recent clicks, cart items, time of day) through a feature assembly layer, then ranks candidates using a lightweight scoring model that responds in under 50ms. Built for product teams powering homepage feeds, product suggestions, or content rankings that adapt to user behavior in real time.
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Pre-computed recommendation candidates are stored in ElastiCache for instant retrieval. Kinesis captures real-time user events (clicks, views, purchases) that update user feature vectors in DynamoDB. The scoring service on ECS scales horizontally with request-level autoscaling. OpenSearch provides content-based filtering as a fallback for cold-start users. Batch model retraining runs daily and publishes new scores to the cache.
Multi-Agent AI System
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LLM Inference Pipeline
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Fine-Tuning Pipeline
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RAG AI Knowledge Base
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Vector Database System
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Model Serving Platform
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Real-Time Recommendation Pipeline
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