Basic service performance tuning New Relic and the python profiler

A review of some basic service performance tuning using New Relic and the python profile tools

READ MORE →

What Can 75,000 Pull Requests Tell About Lyst?

An exploratory analysis of code review practices at Lyst.

READ MORE →

Hackday 2020

Lyst Hackday that we run on December 2020.

READ MORE →

Altering a Postgres Column with Minimal Downtime

Understand dependency and locks to minimise migration downtime

READ MORE →

A machine learning model to understand fashion search queries

Understanding what the user means + mapping that to our inventory

READ MORE →

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

Top tips for running a data science journal club

READ MORE →

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.

READ MORE →

Microservices at Lyst

What we’ve built

READ MORE →

PyData London 2016

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

READ MORE →

Our bug bounty

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

READ MORE →

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.

READ MORE →

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.

READ MORE →

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.)

READ MORE →

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.

READ MORE →

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.

READ MORE →

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.

READ MORE →

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.

READ MORE →

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.

READ MORE →

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.

READ MORE →

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.

READ MORE →

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”.

READ MORE →

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.

READ MORE →

Word Embeddings For Fashion

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

READ MORE →

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.

READ MORE →

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.

READ MORE →

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.

READ MORE →

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.

READ MORE →