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How To Organise Your Machine Learning Teams For Success
The three organization forms Uber, Linkedin and AirBnb explored to integrate machine learning into the organization
Uber, LinkedIn, and Airbnb all are massive machine learning success stories, not because they produce cool research or got lots of talent, but because they actually manage to turn data into money, and lots of it.
All three companies spent years exploring different ways of organizing machine learning work.
I’d like to share the key insights that I took away from their efforts, so you don’t have to spend years exploring different models.
Three Ways to Organize Machine Learning Work
There are really just three ways of organizing machine learning work, and all three have strengths & weaknesses that can either put your machine learning engineers out of work or make them the most valuable asset you have.
So let’s take a look at the three different ways: functionally — within one large machine learning or data science or analytics function, decentralized — within a product/ engineering team, or a mixed form of both.