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netflixtechblog.medium.com
by Roger Menezes, Rahul Jha, Gary Yeh, and Sudarshan Lamkhede In this blog post, we share system design lessons from consolidating several related machine learning models for large-scale search and recommendation systems at Netflix into a single unified model. Given different recommendation use cases, many recommendation systems treat each use-case as a separate machine-learning task and train a b
Operating a large-scale recommendation system is a complex undertaking: it requires high availability and throughput, involves many services and teams, and the environment of the recommender system changes every second. For example, new members or new items may come to the service at any time. New code and new ML models get deployed to production frequently. One question we need to address at Netf
By Josh Snyder The Netflix Cloud Data Engineering team is delighted to open source s3-flash-bootloader, our tool for performing in-place OS image upgrades on stateful cloud instances, substituting a new AMI where an old one was. In this post, I’ll cover some of the context which led us to develop this tooling, and discuss how it has sped up upgrades of Cassandra and Elasticsearch by an order of ma
By Andy Glover and Katharina Probst Over the past four years, Netflix has gone from less than 50 Million subscribers to 125 Million subscribers. While this kind of growth has caused us no shortage of scaling challenges, we actually managed to improve the overall availability of our service in that time frame. Along the way, we have learned a lot and now have a much better understanding of what it
Data Gateway — A Platform for Growing and Protecting the Data Tier
IntroductionWith the deluge of deep learning libraries and software innovation in the field over the last few years, it is an exciting time to be working on machine learning problems. Most of the libraries available evolved from fairly specialized computational code for large dense problems such as image classification into general frameworks for neural-network-based models offering marginal suppo
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