Real-time Recommendations using Spark Comcast LabsDatabricks is the Data and AI company. More than 10,000 organizations worldwide — including Block, Comcast, Conde Nast, Rivian, and Shell, and over 60% of th...
LINE DEVELOPER DAY 2016 開催のお知らせ « LINE Engineers' Blog
LINE株式会社は、2023年10月1日にLINEヤフー株式会社になりました。LINEヤフー株式会社の新しいブログはこちらです。 LINEヤフー Tech Blog saegusa2017-04-16Yoshihiro was a network engineer at LINE, responsible for all levels of LINE's infrastructure. Since being named Infra Platform Department manager, he is finding ways to apply LINE's technology and business goals to the platform. こんにちは。LINEでネットワークやデータセンターを担当している三枝です。2017年1月にJANOG39で登壇する機会を頂きましたので、今回
Open Sourcing Photon ML
Machine learning is a key component of LinkedIn’s relevance-driven products. We use machine learning to train the ranking algorithms for our feed, advertising, recommender systems (such as People You May Know), email optimization, search engines, and more. For an in-depth example, check out these posts (part one and two) on how LinkedIn applies machine learning for ranking the feed. These algorith
Introduction to Machine Learning on Apache Spark MLlib
Speaker: Juliet Hougland, Senior Data Scientist, Cloudera Spark MLlib is a library for performing machine learning and associated tasks on massive datasets. With MLlib, fitting a machine-learning model to a billion observations can take only a few lines of code, and leverage hundreds of machines. This talk will demonstrate how to use Spark MLlib to fit an ML model that can predict which customers
Distributed Time Travel for Feature Generation
We want to make it easy for Netflix members to find great content to fulfill their unique tastes. To do this, we follow a data-driven algorithmic approach based on machine learning, which we have described in past posts and other publications. We aspire to a day when anyone can sit down, turn on Netflix, and the absolute best content for them will automatically start playing. While we are still wa
広告配信現場で使うSpark機械学習
GMO プライベート DMP で ビッグデータ解析をするために アプリクラウドで Apache Spark の検証をしてみた
Machine Learning with Clojure and Spark using Flambo
Machine Learning with Clojure and Spark using Flambo In this short tutorial I’m going to show you how to train a logistic regression classifier in a scalable manner with Apache Spark and Clojure using Flambo. Assumptions: you are familiar with Clojure and Leiningenyou have heard of, or ideally - poked around Apache Sparkyou possess some basic Machine Learning skillsThe goal of the tutorial is to h
KeystoneML
KeystoneML is a software framework, written in Scala, from the UC Berkeley AMPLab designed to simplify the construction of large scale, end-to-end, machine learning pipelines with Apache Spark. We contributed to the design of spark.ml during the development of KeystoneML, so if you’re familiar with spark.ml then you’ll recognize some shared concepts, but there are a few important differences, part
Midwest.io 2014 - Building a Production Machine Learning Infrastructure - Josh Wills
This talk was given at Midwest.io 2014. Cloudera's Data Science Team has a simple mission: build an analytics infrastructure so awesome that it makes Google's Ads Quality Team seethe with jealousy. To that end, I'll give an overview of Cloudera's current data science tools, including Oryx and Spark for building and serving machine learning models, Gertrude for multivariate testing, and Impala for
Music Recommendations at Scale with Spark
The document discusses the development of music recommendation systems at Spotify, primarily focusing on collaborative filtering and matrix factorization techniques. It details methods such as explicit and implicit matrix factorization, as well as the application of Hadoop and Spark for scaling these recommendations. The presentation highlights the evolution of these algorithms and the challenges
Apache Mahout, Hadoop’s original machine learning project, is moving on from MapReduce – Old GigaOm
Apache Mahout, a machine learning library for Hadoop since 2009, is joining the exodus away from MapReduce. The project’s community has decided to rework Mahout to support the increasingly popular Apache Spark in-memory data-processing framework, as well as the H2O engine for running machine learning and mathematical workloads at scale. While data processing in Hadoop has traditionally been done u
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GitHub - OryxProject/oryx: Oryx 2: Lambda architecture on Apache Spark, Apache Kafka for real-time large scale machine learning
Sparkling Water = H20 + Apache Spark
Real-time Recommendations using Spark Comcast Labs