The Netflix TechBlog
JULY 15, 2021
Data Engineers of Netflix?—?Interview Interview with Kevin Wylie This post is part of our “Data Engineers of Netflix” series, where our very own data engineers talk about their journeys to Data Engineering @ Netflix. Kevin, what drew you to data engineering?
The Netflix TechBlog
MARCH 7, 2024
The Machine Learning Platform (MLP) team at Netflix provides an entire ecosystem of tools around Metaflow , an open source machine learning infrastructure framework we started, to empower data scientists and machine learning practitioners to build and manage a variety of ML systems.
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The Netflix TechBlog
NOVEMBER 22, 2019
Netflix shares how Amazon EC2 Auto Scaling allows its infrastructure to automatically adapt to changing traffic patterns in order to keep its audience entertained and its costs on target. In this talk, we share how Netflix deploys systems to meet its demands, Ceph’s design for high availability, and results from our benchmarking.
The Netflix TechBlog
NOVEMBER 22, 2019
Netflix shares how Amazon EC2 Auto Scaling allows its infrastructure to automatically adapt to changing traffic patterns in order to keep its audience entertained and its costs on target. In this talk, we share how Netflix deploys systems to meet its demands, Ceph’s design for high availability, and results from our benchmarking.
The Netflix TechBlog
MARCH 2, 2021
At Netflix, our data scientists span many areas of technical specialization, including experimentation, causal inference, machine learning, NLP, modeling, and optimization. Together with data analytics and data engineering, we comprise the larger, centralized Data Science and Engineering group.
The Netflix TechBlog
NOVEMBER 22, 2019
Netflix shares how Amazon EC2 Auto Scaling allows its infrastructure to automatically adapt to changing traffic patterns in order to keep its audience entertained and its costs on target. In this talk, we share how Netflix deploys systems to meet its demands, Ceph’s design for high availability, and results from our benchmarking.
All Things Distributed
NOVEMBER 15, 2016
They require teams of data engineers to spend months building complex data models and synthesizing the data before they can generate their first report. Finally, their complex user experiences are designed for power users and not suitable for the fast-growing segment of business users.
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