Jonny Brooks-Bartlett – Data Scientist at deliveroo

They use predictive modelling and machine learning.

Their hypothesis is “if they can show you more relevant places you will spend money”

given a list of restaurants, can they rank them optimally

how do they quantify that?

how do you turn that into a machine learning problem?

list of sessions, 0 if they didnt buy, 1 if they did

use a pointwise approach (ask the question for each item in list)

target variable is ‘how likely are you tobuy’

you then pick attributes to add to a model to see if they have an effect

start simple, doesnt need o be machine learning, can be a heuristic (eg eta + popularity) then itterate

it doesnt have to be perfect

Evaluating models:

offline metrics (evaluating before they in to production)

MRR

precision at k

etc

they use MRR

The actual workflow:

track data, write sql queries, make models, generate MRR, pick the one with the highest MRR

they use circleci and deploy

then they do AB tests and split 50/50, if its better, roll out, else revert.

then itterate. constantly.

they write in python, save models in tesorflow, and then read in production language.

amazon sagemaker – can train models and can deploy at scale too

read googles rules of machine learning.