Meson: Workflow Orchestration for Netflix Recommendations
At Netflix, our goal is to predict what you want to watch before you watch it. To do this, we run a large number of machine learning (ML) workflows every day. In order to support the creation of these workflows and make efficient use of resources, we created Meson. Meson is a general purpose workflow orchestration and scheduling framework that we built to manage ML pipelines that execute workloads
Node.js in Flames
We’ve been busy building our next-generation Netflix.com web application using Node.js. You can learn more about our approach from the presentation we delivered at NodeConf.eu a few months ago. Today, I want to share some recent learnings from performance tuning this new application stack. We were first clued in to a possible issue when we noticed that request latencies to our Node.js application
Using Presto in our Big Data Platform on AWS
by Eva Tse, Zhenxiao Luo, Nezih Yigitbasi @ Big Data Platform team At Netflix, the Big Data Platform team is responsible for building a reliable data analytics platform shared across the whole company. In general, Netflix product decisions are very data driven. So we play a big role in helping different teams to gain product and consumer insights from a multi-petabyte scale data warehouse (DW). Th
Distributed Neural Networks with GPUs in the AWS Cloud
by Alex Chen, Justin Basilico, and Xavier Amatriain As we have described previously on this blog, at Netflix we are constantly innovating by looking for better ways to find the best movies and TV shows for our members. When a new algorithmic technique such as Deep Learning shows promising results in other domains (e.g. Image Recognition, Neuro-imaging, Language Models, and Speech Recognition), it
Netflix Recommendations: Beyond the 5 stars (Part 1)
by Xavier Amatriain and Justin Basilico (Personalization Science and Engineering) In this two-part blog post, we will open the doors of one of the most valued Netflix assets: our recommendation system. In Part 1, we will relate the Netflix Prize to the broader recommendation challenge, outline the external components of our personalized service, and highlight how our task has evolved with the busi
Netflix Recommendations: Beyond the 5 stars (Part 2)
by Xavier Amatriain and Justin Basilico (Personalization Science and Engineering) In part one of this blog post, we detailed the different components of Netflix personalization. We also explained how Netflix personalization, and the service as a whole, have changed from the time we announced the Netflix Prize. The $1M Prize delivered a great return on investment for us, not only in algorithmic inn
System Architectures for Personalization and Recommendation
by Xavier Amatriain and Justin Basilico In our previous posts about Netflix personalization, we highlighted the importance of using both data and algorithms to create the best possible experience for Netflix members. We also talked about the importance of enriching the interaction and engaging the user with the recommendation system. Today we’re exploring another important piece of the puzzle: how
Announcing Suro
To make the best business and technical decisions, it is critical for Netflix to reliably collect application specific data in a timely fashion. At Netflix we deploy a fairly large number of AWS EC2 instances that host our web services and applications. They collectively emit more than 1.5 million events per second during peak hours, or around 80 billion events per day. The events could be log mes
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