At Delivery Hero we serve images of food to millions of customers every day. Our centralized image service handles over a billion requests per month and serves these to hungry customers in over 20 countries. We see improvements to our image services as part of lowering page weight, and ultimately part of a better customer experience. While building our image service, we researched ways to minimize transmission time with a particular focus on image formats.
In this article, I will introduce how and why our team here at foodora/foodpanda is exposing an API that makes machine learning predictions using R, AWS Lambda and Amazon API Gateway. I will guide you through all the required steps while using the prediction of the food preparation time of our restaurants.
On the 1st and 2nd of March 2018 the first GrafanaCon on European territory took place in the Compagnietheater in Amsterdam, Netherlands. I’ve had the pleasure of being among the attendees and would like to share some of the highlights and ideas that have been presented at the conference.
At Lieferheld and Pizza.de, most of our server-side platform code was written in Python 2.7 until last year. I want to share some of our experience migrating elements of this codebase to Python 3 and, in particular, taking advantage of the asyncio module and native coroutines available in Python 3.5.
Having used NewRelic for more than 5 years, at Delivery Hero we only just recently started taking advantage of a few pretty awesome, but so far unknown/ignored features. Let me quickly go through them:
On Tuesday evening Delivery Hero welcomed many fellow “Gophers” to our Headquarters. Ever since we started to adopt Golang in early 2017, many of us have attended the monthly meetups of the Berlin Golang community. Therefore it was a great pleasure for us to host the event ourselves for the first time. We shared great food from Habba Habba as well as interesting talks with like minded people.