semi_sentient

4 ms·

1.
by semi_sentient 11mo ago
Follow-up posts for context (same series): Part 2 – Data Layer (feature store to prevent online/offline skew; vector DB choices and pre- vs post-filtering): https://www.shaped.ai/blog/the-infrastructure-of-modern-r
2.
The infrastructure behind modern ranking systems (serving, data, MLOps)(shaped.ai)
1 points by semi_sentient 11mo ago|2 comments
3.
by semi_sentient 11mo ago
Modern ranking systems (feeds, search, recommendations) have strict latency budgets, often under 200 ms at p99. This write-up describes how we designed a production system using a decoupled microservice architecture for serving, a feature +
4.
by semi_sentient 11mo ago
Retrieval is the stage where a ranking system narrows billions of items down to a few hundred candidates, fast enough for real-time use. It’s the least visible but most constrained layer: latency budgets, freshness, and recall all collide h
5.
How Retrieval Works in Modern Ranking Systems(shaped.ai)
1 points by semi_sentient 11mo ago|1 comments
6.
by semi_sentient 11mo ago
A concise explainer of the standard four-stage architecture used in most modern recommendation and ranking systems: retrieval, scoring, ordering, and feedback. It walks through how these stages connect in production systems like search, fee
7.
How Modern Ranking Systems Work(shaped.ai)
3 points by semi_sentient 11mo ago|1 comments
8.
by semi_sentient 1y ago
shrug maybe the solution on something like this is where every nth post is a personalized one.
9.
by semi_sentient 1y ago
agreed, but looking at the article it looks like you can turn this personalization bit up or down though to find the mama bear/just right level?
10.
Action Is All You Need: Dual-Flow Generative Ranker 4× Faster and More Accurate(shaped.ai)
1 points by semi_sentient 1y ago|0 comments