I show calibration curves for four different binary classification Scikit-Learn models we built for delivery prediction at Fetchr, trained using real-world data: LogisticRegression, DecisionTree, RandomForest and GradientBoosting.

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Opportunities for automating, optimizing and enabling processes with ML at a delivery company such as Fetchr are plentiful. We put three families of ML models into production. These 3 areas are: Scheduling, Notifications and Operational choice.

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The idea is simple: write a document which helps new and existing people—both managers and individual contributors—get an objective, metrics-based picture of the business. This is helpful when new people join, when people start working in new segments of the business, and to understand other parts of the company.

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While working on arabic-vs-rest classification, I was curious how good out-of-the-box models perform with publicly available data, and then compare that with what we can achieve with internal data / features derived from millions of deliveries. We train Scikit-learn and Pytorch models for this classification task and achieve 90% prediction accuracy on publicly available data and out-of-the-box models, while internally 99% is achievable.

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Most A/B testing tools default to α=0.05, meaning the expected false positive rate is 5%. In this post I explore the trade-offs between moving fast, ie. using higher α, versus being sure, ie. using lower α.

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I was grabbing a burger at Shake Shack, Mall of the Emirates in Dubai, when I noticed this notebook on the counter. The staff is using it to track food deliveries and each service (Carriage, Talabat, UberEats, Deliveroo) has its own column with the order numbers. Let's assume this is the only page for the day, and ask ourselves: given this data, what is the probability that UberEats is the most popular food delivery service?.

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Over a period of 6 months, we rolled out a Machine Learning model to predict a customer’s delivery (latitude, longitude). During the recent holiday peak, this ML model handled most of Fetchr’s order scheduling.

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Sometimes, the seven gods of data science, Pascal, Gauss, Bayes, Poisson, Markov, Shannon and Fisher, all wake up in a good mood, and things just work out. Recently we had such an occurence at Fetchr, when the Operational Excellence team posed the following question: if we could pick our Saudi warehouse locations, where would be put them? What is the ideal number of warehouses, and, what does ideal even mean? Also, what should our “delivery radius” be?

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Previously I wrote two articles about data infra and data engineering at Fetchr. This time I want to move up the stack and talk about a simple piece of metrics engineering that proved to be very impactful: Growth Accounting and Backtraced Growth Accounting.

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A description of our Analytics+ML cluster running on AWS, using Presto, Airflow and Superset.

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We used Hive/Presto on AWS together with Airflow to rapidly build out the Data Science Infrastructure at Fetchr in less than 6 months.

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