Most products don’t have the luxury of retaining users beyond the first session solely on their solid reputations, and in order to do so, we need to optimize for the best user experience possible.
Have you recently launched a cool new feature you spent so much time on? What data do you use to determine the impact of the feature? Take a look at this blog, this may be just for you!
Ted Lasso has some real-life lessons we can all take in. How is this possible? Take a look!
Why is it important to remove underperforming features to improve the product’s key metrics? Find out here.
We wanted to see if there was a way we could sync our Kubernetes NetworkPolicies dynamically with tools we already use, like Consul and Calico.
Read this article to learn more about what conversions are, how Taboola handle billions of daily events at scale, and how it all presents meaningful data to customers.
Something strange happened while I worked with Kafka. While adding a new consumer from Kafka to one of our services, the service stopped consuming from ALL other existing consumers. As part of my job at Taboola as a team leader on a production team in the Infrastructure group, we’re supposed to remove bottlenecks, not create them. This post will describe how I investigated the issue, explain what I discovered, and share my insights into the whole situation. Some background Before I get into the rest of the story, here’s some background on how we use Kafka at Taboola’s events handling pipeline and why it’s critical to our infrastructure. Taboola’s recommendations appear on tens of thousands of web pages and mobile apps every second. As users engage with the content, multiple events are fired to signal that recommendations are rendered, opened, clicked, and so on. Each event triggers one or more Kafka messages, […]
Find out the secrets to how Taboola deploys and manages the thousands of servers that bring you recommendations every day.
Taboola is one of the largest content recommendation companies in the world. We maintain hundreds of servers in multiple data centers around the world, while obligated to strict SLAs. Thus, you might understand why our engineers would appreciate a little heads up when the system gets overloaded. Like most companies today, we use metrics to visualize our services’ health, and our challenge is to create an automatic system that will detect issues in multiple metrics as soon as possible, without any performance impact. A real life example Wouldn’t it be nice if we could predict the impact on our response time metric when major events are about to happen? For example “Black Friday”, “Cyber Monday” or even the “Kobe Bryant’s tragedy”, on the 26/1/2020? – as can be seen below: Figure 1: Kobe Bryant’s downtime 26/1/20 And yes – the gap with no metrics around the 26/1 is the downtime […]
At Taboola, we work daily on improving our Deep-Learning-based content-recommendation model. We use it to suggest personalized news articles and ads to hundreds of millions users a day, so naturally we must stick to state-of-the-art deep learning modeling methods. But our job doesn’t end there – analyzing our results is a must too, and then we sometimes return to our data science roots and apply some very basic techniques. Let’s lay such a problem out. We are investigating a deep model that behaves rather strangely: it wins over our default model for what looks like a random group of advertisers, and loses for another group. This behavior is stable in the day to day, so it looks like there might be some inherent advertisers qualities (what we’ll call – campaign features) to blame for this. You can see a typical model behavior for 4 campaigns below. So we hypothesize that […]