Altering a Postgres Column with Minimal Downtime

Understand dependency and locks to minimise migration downtime

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Django URLconf Export

Make URLs for your Django website from anywhere

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A machine learning model to understand fashion search queries

Understanding what the user means + mapping that to our inventory

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How to run a data science journal club that your team actually engages with

Top tips for running a data science journal club

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Working with Fashion Models

Being a fashion company we often have to work with temperamental high-maintenance models, by which of course I mean machine learning models and not the human variety.

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A command line tool for your organisation

Dealing with too many tools.

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Microservices at Lyst

What we’ve built

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PyData London 2016

The Lyst Data Science team was out in force at PyData London this weekend.

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Practical Design Principles

As a design team at Lyst, we believe it's really important to be self-critical in order to improve. One of the ways we are doing this is by writing down some design principles to keep us on track and improving.

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Our bug bounty

The one where we give you money in exchange for bugs.

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Django Session store and DB Router

How we moved our 70GB of sessions data into a new store with a custom SessionStore class and a db router.

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Tools That Made Our Microservices Easier

How we've handled the move to microservices at Lyst: from a single codebase to a collection of small software services. We also discovered the tools for any engineer to quickly build and deploy their own services.

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Sharing Our Best Practices

Similarly to modern web services, best practices constantly iterate and improve. Here at Lyst we’ve taken many of our favourite practices and tried to adopt them. As we’ve grown our team and started to follow the practices, we’ve been tweaking them to make them better suited based on how we work. We’ve also been asking new team members to share their previous experiences and opinions on what works well for various aspects in our team. This has been really good for us and we’ve been wondering how we could get more of this outside influence.

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Getting to grips with Selenium

At Lyst we’ve been improving our testing environments over the last year or so, and one of the main elements we wanted to improve was our testing stack with Selenium. We’ve used Selenium in the past, but the tests grew old, were poorly maintained, and few people could work out how they worked after our shift to Docker (read more about that in a previous post.)

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What I Do at Lyst: Lotfi Bentouati

We’re back with another What I Do post. This time we chat with one of our Operations Engineers, Lotfi Bentouati.

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I Look Like an Engineer

Unless you've been under a rock in the Twitter world for the last week - you will have seen the #ILookLikeAnEngineer hashtag. Here at Lyst, we have some brilliant engineers - many of whom are women. We decided we should tell you all a little bit more about ourselves, how we came to engineering, and what advice we have for women wanting to be engineers themselves.

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Pyntxos and Sunshine: Our week at EuroPython

Last week we shipped off to Spain for a week at EuroPython. Even before we had started to properly travel, we started spotting other Python developers.

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What I Do at Lyst: Sandra Greiss

This is the second post in our series of sharing what we do at Lyst. This time I’d like to introduce Sandra Greiss, one of our Junior Data Scientists.

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What I Do at Lyst: Ivan Prignano

Nearly half of the staff at Lyst are technical or have a technical background. We have a large technology stack and plenty of exciting projects that we’re working on. But we’re often so focused on developing great experiences that we don’t get the time to share what we’re doing with you.

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Lyst at EuroPython 2015

Our engineering team is taking a short hop to mainland Europe this July to attend EuroPython 2015 in sunny Bilbao, Spain. We’ll be spending six days with fellow Pythonistas from all across Europe (and even the world!) and attending over 200 sessions, workshops, and social events.

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Searching for Approximate Nearest Neighbours

Nearest neighbour search is a common task: given a query object represented as a point in some (often high-dimensional) space, we want to find other objects in that space that lie close to it. For example, a mapping application will perform a nearest neighbours search when we ask it for restaurants close to our location.

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ICLR 2015

ICLR is a relatively new conference that is primarily concerned with deep learning and learned representations. The conference is into its third year and had over 300 attendees, two of which were from Lyst. In this post we’ll discuss a few of the interesting papers and themes presented this year.

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How we do UI style at Lyst

The iOS team at Lyst has been opening and sharing it’s knowledge over the last months on different topics: lean approach, coding standards.

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Why we have First Thursdays

On the first Thursday of each month, we, the whole engineering team at Lyst, gather together to share ideas, experiences, learnings, drinks and snacks under the title of “First Thursdays”.

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1 to 1 relationships and subresources in REST APIs

For the past few years I have advocated best practices for building REST APIs and I spent a lot of time building reasonably well designed examples to help demonstrate it. I learned that building REST APIs from the ground up isn’t hard at all because you have no legacy or technical debt to work with, so of course everything is going to work well and be praised for being RESTful.

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Giving in to BEM

One of our New Year’s resolutions here at Lyst is to improve the structure and maintainability of our CSS and we will be doing this by adopting the BEM methodology. This post originally appeared on jonbretman.co.uk.

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How to win an Internet of Things hackday if you don't know your Arduino from your Beaglebone

I've never really got into Arduinos, Raspberry Pis and the like, and haven't touched a breadboard for 12 years. Despite this, I won a hardware hackday at the recent AWS re:Invent conference.

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Docker at Lyst

We’re not quite finished rolling it out to all our services but we’ve learnt a lot of lessons and it’s had a big impact on how we work.

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Word Embeddings For Fashion

In which we apply word embedding techniques to our corpus of fashion data.

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AB Testing With Continuous Outcomes (And Horribly Misspecified Priors)

Bayesian analysis of A/B tests is a great way of getting reliable inference. Except, of course, when we get our priors horribly wrong.

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OpenRoss - fast, scalable, on-demand image resizer

The OpenRoss image service provides a way of serving dynamically resized images from Amazon S3 in a way that is fast, efficient, and auto-scales with traffic.

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Bayesian AB Testing

A/B testing is a great tool in identifying the effect of incremental changes on the behaviour of users. Using Bayesian methods to analyze the results will help us to draw more robust conclusions from the data.

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Unit Testing JavaScript

There are many ways to implement unit testing for JavaScript code and lots of frameworks to choose from. This post describes the setup we use at Lyst as well as why we chose the particular libraries / tools we did.

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Color Detection

We process millions of fashion products a day from over 500 retailers. One of the goals of the data-team is to transform this stream of semi-structured data into one consistent product catalogue. Colour is one of the most difficult fields to normalise. In this post we discuss how product colors are derived from product images.

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Image Background Removal

We process millions of images using an ecosystem of classifiers. In order to get the most information out of an image, it is best to remove the background as it may contain data which will make the classifier less accurate. In this post we discuss methods of removing backgrounds from images.

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Hello World

Lyst is an data-driven online fashion market-place. All products on site are retrieved dynamically from the web via scraping, feeds and APIs. In this blog we will discuss some of the engineering challenges we face and some of the solutions we devise.

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