My three favorite papers, 17 paper summaries, and ML and non-ML lessons.
Invited keynote at the Workshop for Online Recommender Systems and User Modeling (ORSUM)
Or why I should write fewer integration tests.
Pushing back on the cult of complexity.
Simple baselines, ideas, tech stacks, and packages to try.
An overview of system design, candidate retrieval, and ranking, with industry examples.
Why real-time RecSys? What does the system design look like in industry? How to build an MVP?
Breaking it into offline vs. online environments, and candidate retrieval vs. ranking steps.
An overview and comparison of the various approaches, with examples from industry search systems.
Design and architecture, tech stack, methodology, results, and lessons learned.
Why real-time? How have China & US companies built them? How to design & build an MVP?
Emphasis on bias, more sequential models & bandits, robust offline evaluation, and recsys in the wild.
Part II of the previous write-up, this time on applications and frameworks of Spark in production
After this article, we'll have a workflow of tests and checks that run automatically with each git push.
A curious discussion made me realize my expert blind spot. And no, Airflow is not late.
Can maintaining machine learning in production be easier? I go through some practical tips.
I thought deploying machine learning was hard. Then I had to maintain multiple systems in prod.
In-depth sharing on how to put machine learning systems into production.
How we built an ML system to predict hospitalization costs at admission; sharing at DATAx Conference.
Or how to put machine learning models into production.
A web app to find similar products based on image.
How Lazada ranks products to improve customer experience and conversion at Strata 2016.