Tag:
teardown
(12)Improving Recommendation Systems & Search in the Age of LLMs
Model architectures, data generation, training paradigms, and unified frameworks inspired by LLMs.
Push Notifications: What to Push, What Not to Push, and How Often
Sending helpful & engaging pushes, filtering annoying pushes, and finding the frequency sweet spot.
24 Dec 2023 Ā· 18 min Ā· teardown recsys machinelearning production
Content Moderation & Fraud Detection - Patterns in Industry
Collecting ground truth, data augmentation, cascading heuristics and models, and more.
26 Feb 2023 Ā· 16 min Ā· teardown machinelearning production
Bandits for Recommender Systems
Industry examples, exploration strategies, warm-starting, off-policy evaluation, and more.
08 May 2022 Ā· 14 min Ā· teardown recsys machinelearning
Reinforcement Learning for Recommendations and Search
Focusing on long-term rewards, exploration, and frequently updated item.
05 Sep 2021 Ā· 13 min Ā· teardown recsys deeplearning
Bootstrapping Labels via ___ Supervision & Human-In-The-Loop
How to generate labels from scratch with semi, active, and weakly supervised learning.
01 Aug 2021 Ā· 12 min Ā· teardown machinelearning
System Design for Recommendations and Search
Breaking it into offline vs. online environments, and candidate retrieval vs. ranking steps.
27 Jun 2021 Ā· 13 min Ā· teardown production engineering recsys š„
Patterns for Personalization in Recommendations and Search
A whirlwind tour of bandits, embedding+MLP, sequences, graph, and user embeddings.
13 Jun 2021 Ā· 25 min Ā· teardown recsys machinelearning deeplearning
Search: Query Matching via Lexical, Graph, and Embedding Methods
An overview and comparison of the various approaches, with examples from industry search systems.
25 Apr 2021 Ā· 21 min Ā· teardown machinelearning production š„
Feature Stores: A Hierarchy of Needs
Access, serving, integrity, convenience, autopilot; use what you need.
21 Feb 2021 Ā· 19 min Ā· teardown machinelearning engineering š„
Real-time Machine Learning For Recommendations
Why real-time? How have China & US companies built them? How to design & build an MVP?
10 Jan 2021 Ā· 21 min Ā· teardown machinelearning recsys production š„
Data Discovery Platforms and Their Open Source Solutions
What questions do they answer? How do they compare? What open-source solutions are available?
25 Oct 2020 Ā· 16 min Ā· teardown datascience engineering
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