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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The Collatz conjecture is a conjecture in mathematics that concerns a sequence defined as follows: start with any positive integer n. Then each term is obtained from the previous term as follows: if the previous term is even, the next term is one half the previous term. If the previous term is odd, the next term is 3 times the previous term plus 1. The conjecture is that no matter what value of n, the sequence will always reach 1.

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It’s easy to build a CNN that does well on MNIST digit classification. How easy is it to break it, to distort the images and cause the model to misclassify?

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CIFAR-10 is a classic image recognition problem, consisting of 60,000 32x32 pixel RGB images (50,000 for training and 10,000 for testing) in 10 categories: plane, car, bird, cat, deer, dog, frog, horse, ship, truck. Convolutional Neural Networks (CNN) do really well on CIFAR-10, achieving 99%+ accuracy. The Pytorch distribution includes an example CNN for solving CIFAR-10, at 45% accuracy. I will use that and merge it with a Tensorflow example implementation to achieve 75%. We use torchvision to avoid downloading and data wrangling the datasets. Like in the MNIST example, I use Scikit-Learn to calculate goodness metrics and plots.

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MNIST is a classic image recognition problem, specifically digit recognition. It contains 70,000 28x28 pixel grayscale images of hand-written, labeled images, 60,000 for training and 10,000 for testing. Convolutional Neural Networks (CNN) do really well on MNIST, achieving 99%+ accuracy. The Pytorch distribution includes a 4-layer CNN for solving MNIST. Here I will unpack and go through this example. We use torchvision to avoid downloading and data wrangling the datasets. Finally, instead of calculating performance metrics of the model by hand, I will extract results in a format so we can use SciKit-Learn's rich library of metrics.

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I use the standard Iris dataset for supervised learning with a Support Vector Machine model using Pytorch's autograd.

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A PyTorch model is trained on public Hacker News data, embedding posts and comments into a high-dimensional vector space, using the mean squared error (MSE) of dot products as the loss function. The resulting model is reasonably good at finding similar posts and recommending posts for users.

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rxe is a thin wrapper around Python's re module. The various rxe functions are wrappers around corresponding re patterns. For example, rxe.digit().one_or_more('a').whitespace() corresponds to \da+\s. Because rxe uses parentheses but wants to avoid unnamed groups, the internal (equivalent) representation is actually \d(?:a)+\s. This pattern can always be retrieved with get_pattern().

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I will show how to solve the standard A x = b matrix equation with PyTorch. This is a good toy problem to show some guts of the framework without involving neural networks.

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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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2018 was a hot year for Data Science and AI. Here we picked out 5 highlights, which in our opinion shaped the field in the past year.

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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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Warren Buffett says deciding what not to spend time on is just as important as deciding what to spend time on.

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When working with averages, we have to be careful. There are pitfalls lurking to pollute our statistics and results reported.

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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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I used to think that a good analogy for using data is the instrumentation of a cockpit in an airliner. Lots of instruments, and if they fail, the pilot can’t fly the plane and bad things happen. There’s no autopilot for companies. The problem with this analogy is that planes aren’t built in mid-air. Product teams and companies constantly need to build and ship new products.

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I gave this talk at the O’Reilly Strata Conference London in 2016 June, mostly based on what I learned at Prezi from 2012-2016.

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I read this book on my first vacation after I started working at Facebook and thus became a semi-regular Hack/HHVM user. I highly recommend reading (parts of) it. But not to learn Hack/PHP, which is irrelevant to most people. Instead, it’s to learn about how Facebook improved it’s www codebase and performance without rewriting the old PHP code in one big effort, and thus avoided the famous Second-system effect.

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