BLUEPRINT: MULTISTAGE MULTIMODAL RECSYS ON AMAZON EKS WITH TRITON AND IN‑MEMORY FEATURE CACHING
A practitioner walks through building and shipping a multistage multimodal recommender on Amazon EKS using Triton, Kubeflow, Bloom filters, and in‑memory featur...
A practitioner walks through building and shipping a multistage multimodal recommender on Amazon EKS using Triton, Kubeflow, Bloom filters, and in‑memory feature caching.
This end‑to‑end guide shows a four‑stage pipeline—Two‑Tower retrieval, Bloom‑filter suppression, DLRM ranking, and final rerank—plus how CLIP and Sentence‑BERT embeddings feed candidate generation. It also details serving 14 models on NVIDIA Triton, autoscaling on EKS, and a notable p99 win from in‑memory feature caching article.
If you own a recsys stack, the patterns here are concrete: feature store + hot cache, contextual features at rank time, and Triton model orchestration on Kubernetes. It’s a practical reference you can lift into your stack read.
Shows a proven way to cut rank-time feature lookup latency while scaling multi-model inference on Kubernetes.
Offers a practical template for contextual, near‑real‑time recommendations without a full platform rebuild.
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terminal
Measure cache hit rate vs. p95/p99 latency for feature lookups; tune TTL/eviction and warmup strategy.
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terminal
Benchmark Triton ensemble (14 models) vs. separate microservices for QPS, GPU/CPU utilization, and tail latency.
Legacy codebase integration strategies...
- 01.
Layer in-memory feature caching in front of your feature store and A/B the Bloom-filter suppression against your current dedupe/recency logic.
- 02.
Pilot Triton for a subset of models; compare autoscaling behavior and observability with your existing inference stack.
Fresh architecture paradigms...
- 01.
Start with Two‑Tower retrieval + DLRM ranker and add rerank later; use Triton for model consolidation from day one.
- 02.
Adopt Kubeflow for train/serve pipelines and design feature schemas for both offline store and hot cache access.
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