Etsy Engineering | Measure Anything, Measure Everything
If Engineering at Etsy has a religion, it's the Church of Graphs. If it moves, we track it. Sometimes we'll draw a graph of something that isn't moving yet, just in case it decides to make a run for it. In general, we tend to measure at three levels: network, machine, and application. (You can read more about our graphs in Mike's Tracking Every Release post.) Application metrics are usually the ha
Double-bucketing in A/B Testing - Code as Craft
Previously, we’ve posted about the importance we put in Etsy’s experimentation systems for our decision-making process. In a continuation of that theme, this post will dive deep into an interesting edge case we discovered. We ran an A/B test which required a 5% control variant and 95% treatment variant rather than the typical split of 50% for control and treatment variants. Based on the nature of
boundary-layer : Declarative Airflow Workflows - Code as Craft
When Etsy decided last year to migrate our operations to Google Cloud Platform (GCP), one of our primary motivations was to enable our machine learning teams with scalable resources and the latest big-data and ML technologies. Early in the cloud migration process, we convened a cross-functional team between the Data Engineering and Machine Learning Infrastructure groups in order to design and buil
Design for Continuous Experimentation
In an ever-changing landscape of one digital disruption after another, companies and organisations are looking for new ways to understand their target markets and engage them better. Increasingly they invest in user experience (UX) and customer experience design (CX) capabilities by working with a specialist UX agency or developing their own UX lab. Some UX practitioners are touting leaner and fas
Images Don’t Lie: Transferring Deep Visual Semantic Features to Large-Scale Multimodal Learning to R
Home / Topics Images Don’t Lie: Transferring Deep Visual Semantic Features to Large-Scale Multimodal Learning to Rank Corey Lynch, ; Kamelia Aryafar*, Etsy Inc.; Josh Attenberg, Etsy Abstract Search is at the heart of modern e-commerce. As a result, the task of ranking search results automatically (learning to rank) is a multibillion dollar machine learning problem. Traditional models optimize ove
Etsy Engineering | Introducing statsd-jvm-profiler: A JVM Profiler for Hadoop
At Etsy we run thousands of Hadoop jobs over hundreds of terabytes of data every day. When operating at this scale optimizing jobs is vital: we need to make sure that users get the results they need quickly, while also ensuring we use our cluster’s resources efficiently. Actually doing that optimizing is the hard part, however. To make accurate decisions you need measurements, and so we have cr
Etsy Engineering | Sahale: Visualizing Cascading Workflows at Etsy
The Problem If you know anything about Etsy engineering culture, you know we like to measure things. Frequent readers of Code As Craft have become familiar with Etsy mantras like “If it moves, graph it." When I arrived at Etsy, our Hadoop infrastructure, like most systems at Etsy, was well instrumented at the operational level via Ganglia and the standard suite of exported Hadoop metrics, but was
Etsy Engineering | Personalized Recommendations at Etsy
Providing personalized recommendations is important to our online marketplace. It benefits both buyers and sellers: buyers are shown interesting products that they might not have found on their own, and products get more exposure beyond the seller’s own marketing efforts. In this post we review some of the methods we use for making recommendations at Etsy. The MapReduce implementations of all t
Etsy Engineering | Conjecture: Scalable Machine Learning in Hadoop with Scalding
Intro Predictive machine learning models are an important tool for many aspects of e-commerce. At Etsy, we use machine learning as a component in a diverse set of critical tasks. For instance, we use predictive machine learning models to estimate click rates of items so that we can present high quality and relevant items to potential buyers on the site. This estimation is particularly important
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Scalding at Etsy
Here’s a presentation I gave about how Etsy wound up using Scalding for analysis. Given at the San Francisco Cascading Meetup.
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Etsy “going all in” with Kafka as dataflow pipeline | #HPBigData2015 - SiliconANGLE
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Etsy shares more than double in their trading debut
Dan McKinley :: Data Driven Products Now! Etsyで学んだ新機能実装の進め方．
GitHub - etsy/feature: Etsy's Feature flagging API used for operational rampups and A/B testing.
Prantik Bhattacharyya - Biography
https://mimno.infosci.cornell.edu/nips2013ws/nips2013tm_submission_23.pdf
Scalding at Etsy
Scalding at Etsy | Hacker News