production] [ datascience machinelearning learning career python productivity production omscs engineering lazada writing til misc business survey leadership deeplearning recsys agile spark nlp informalmentors ]
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.
A simple web app to classify fashion images into Amazon categories.