The Four Innovation Phases of Netflix’s Trillions Scale Real-time Data Infrastructure
My name is Zhenzhong Xu. I joined Netflix in 2015 as a founding engineer on the Real-time Data Infrastructure team and later led the Stream Processing Engines team. I developed an interest in real-time data in the early 2010s, and ever since believe there is much value yet to be uncovered. Netflix was a fantastic place to be surrounded by many amazing colleagues. I can’t be more proud of everyone
Open-Sourcing Metaflow, a Human-Centric Framework for Data Science
by David Berg, Ravi Kiran Chirravuri, Romain Cledat, Savin Goyal, Ferras Hamad, Ville Tuulos tl;dr Metaflow is now open-source! Get started at metaflow.org. Netflix applies data science to hundreds of use cases across the company, including optimizing content delivery and video encoding. Data scientists at Netflix relish our culture that empowers them to work autonomously and use their judgment to
ML Infra for Netflix Recommendations - AI NEXTCon talk
Faisal Siddiqi presented on machine learning infrastructure for recommendations. He outlined Boson and AlgoCommons, two major ML infra components. Boson focuses on offline training for both ad-hoc exploration and production. It provides utilities for data preparation, feature engineering, training, metrics, and visualization. AlgoCommons provides common abstractions and building blocks for ML like
Python at Netflix
By Pythonistas at Netflix, coordinated by Amjith Ramanujam and edited by Ellen Livengood As many of us prepare to go to PyCon, we wanted to share a sampling of how Python is used at Netflix. We use Python through the full content lifecycle, from deciding which content to fund all the way to operating the CDN that serves the final video to 148 million members. We use and contribute to many open-sou
Making Netflix Machine Learning Algorithms Reliable
This document discusses making Netflix machine learning algorithms reliable. It describes how Netflix uses machine learning for tasks like personalized ranking and recommendation. The goals are to maximize member satisfaction and retention. The models and algorithms used include regression, matrix factorization, neural networks, and bandits. The key aspects of making the models reliable discussed
1
リリース、障害情報などのサービスのお知らせ
最新の人気エントリーの配信
処理を実行中です
j次のブックマーク
k前のブックマーク
lあとで読む
eコメント一覧を開く
oページを開く
Human-Centric Machine Learning Infrastructure @Netflix
