Do you want to know how we work at Lyst? Check out our engineering principles!
A review of some basic service performance tuning using New Relic and the python profile tools
An exploratory analysis of code review practices at Lyst.
Lyst Hackday that we run on December 2020.
Understand dependency and locks to minimise migration downtime
Understanding what the user means + mapping that to our inventory
Top tips for running a data science journal club
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.
The Lyst Data Science team was out in force at PyData London this weekend.
The one where we give you money in exchange for bugs.
How we moved our 70GB of sessions data into a new store with a custom SessionStore class and a db router.
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.
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.)
We’re back with another What I Do post. This time we chat with one of our Operations Engineers, Lotfi Bentouati.
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.
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.
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.
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.
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.
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.
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”.
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.
In which we apply word embedding techniques to our corpus of fashion data.
Bayesian analysis of A/B tests is a great way of getting reliable inference. Except, of course, when we get our priors horribly wrong.
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.
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.
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.