Sept. 3, 2023, 9:59 a.m. | /u/Johann_SebastianBach

Data Science

Hello everyone! I've noticed that most beginner-level tutorials on recommender systems primarily focus on model training, with limited information about deploying them in a production environment. Additionally, the different usage of models in the recall (retrieval) and ranking modules can indeed be confusing for beginners.

Recently, I've been working on a recommender system project that encompasses both offline **development** and online **deployment**, covering both **recall** and **ranking** modules. The entire project is developed using Python and executed on a single …

application beginner build datascience environment focus hello indeed inference information modules production ranking recall recommender systems retrieval systems them training tutorials usage vector web

Senior AI/ML Developer

@ | Remote

Earthquake Forecasting Post-doc in ML at the USGS

@ U. S. Geological Survey | Remote, US

Senior Data Scientist - Remote - Colombia

@ FullStack Labs | Soacha, Cundinamarca, Colombia

Senior Data Engineer

@ Reorg | Remote - US

Quantitative / Data Analyst

@ Talan | London, United Kingdom

Senior Data Scientist

@ SoFi | CA - San Francisco; US - Remote