Machine Learning for Apache Solr, Elasticsearch & Vespa #bbuzz
summary
Compares LTR support across Elasticsearch (third‑party LTR plugin using RankLib/XGBoost), Solr (built‑in LTR contrib module) and Vespa (native tensors + TensorFlow) and shows working demos: indexes a 27,000‑movie TMDB dataset, extracts features, trains 10 trained RankLib models (including LambdaMART) and uploads them to model stores for second‑phase re‑ranking — Elasticsearch and Solr produce the same reordered results — Vespa runs a real personalization use case over 1.1M blog posts + TensorFlow model (20h training).
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chapters (9)
- 0:00Introduction
- 1:35Elastic Search
- 3:05Query D Scoring
- 4:30Demo
- 6:50Feature Store
- 8:10Judgments
- 10:35Model files
- 11:35Learning to Rank for Solr
- 16:45Demos
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