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
The document describes a biolatency tool that traces block device I/O latency using eBPF. It discusses how the tool was originally written in the bcc framework using C/BPF, but has since been rewritten in the bpftrace framework using a simpler one-liner script. It provides examples of the bcc and bpftrace implementations of biolatency.Read less
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
私はここ1週間ほど、同僚の David の一言で Infrastructure as Code について頭が大混乱状態でした。 それは次の一言です。 Chef や Puppet は大体の部分は Infrastructure as Code じゃないよね。ARM (Azure Resource Manager) はそうだけど。 ただ、Chef-Provisioning は Infrastructure as Code だよね。 もう頭が大混乱です。なんとなく言わんとしていることはわかりますが、私は今まで Chef とか、Puppet とか、Ansible とかで やっているようなことが、Infrastructure as Code と思い込んでいましたが、何か間違っていたのでしょうか?そういえば、 Chef はConfiguration Management Toolと紹介されていたなとか頭
(BDT303) Running Spark and Presto on the Netflix Big Data Platform In this session, we discuss how Spark and Presto complement the Netflix big data platform stack that started with Hadoop, and the use cases that Spark and Presto address. Also, we discuss how we run Spark and Presto on top of the Amazon EMR infrastructure; specifically, how we use Amazon S3 as our data warehouse and how we leverage
The Netflix Way to deal with Big Data Problems 1. The way to deal with Big data problems Monal Daxini March 2016 2. Monal Daxini Real Time Data Infrastructure Senior Software Engineer, Netflix https://www.linkedin.com/in/monaldaxini @monaldax #Netflix #Keystone 3. We help Produce, Store, Process, Move Events @ scale 4. Tell me more... ● Big Data Ecosystem @ Netflix ● How we built a scalable event
(BDT318) How Netflix Handles Up To 8 Million Events Per Second In this session, Netflix provides an overview of Keystone, their new data pipeline. The session covers how Netflix migrated from Suro to Keystone, including the reasons behind the transition and the challenges of zero loss while processing over 400 billion events daily. The session covers in detail how they deploy, operate, and scale K
Interactive recommender systems enable the user to steer the received recommendations in the desired direction through explicit interaction with the system. In the larger ecosystem of recommender systems used on a website, it is positioned between a lean-back recommendation experience and an active search for a specific piece of content. Besides this aspect, we will discuss several parts that are
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