My Thoughts On TFXEvaluating MLOps Tools Watch this talk on YouTube Hamel Husain Stanford CS329S ML Systems Design, February 2022
File Format Benchmark - Avro, JSON, ORC & Parquet
This document summarizes a benchmark study of file formats for Hadoop, including Avro, JSON, ORC, and Parquet. It found that ORC with zlib compression generally performed best for full table scans. However, Avro with Snappy compression worked better for datasets with many shared strings. The document recommends experimenting with the benchmarks, as performance can vary based on data characteristic
Apache storm vs. Spark Streaming
2. Honestly... • I know a lot more about Apache Storm than I do Apache Spark Streaming. • I've been involved with Apache Storm, in one way or another, since it was open-sourced. • I'm admittedly biased. 3. But... • A number of articles/papers comparing Apache Storm and Spark Streaming are inaccurate in terms of Storm’s features and performance characteristics. • Code and configuration for those stu
Scala and big data in ICM. Scoobie, Scalding, Spark, Stratosphere. Scalar 2014
This document discusses various big data frameworks including Spark, Scoobi, Hadoop, and GraphX. It provides an example of using Spark to interactively analyze log data stored on HDFS. Spark allows loading data into memory and running multiple queries efficiently. The document also discusses benefits of Spark such as its scalability, libraries for SQL, streaming, machine learning and more.Read les
solrとelasticsearchの比較
elasticsearchの紹介です！ atnd.org/events/33718
Kitchen Soap – Slides from Web2.0 Expo 2009. (and somethin else interestin’)
That was a pretty good time. Saw lots of good and wicked smaht people, and I got a lot of great questions after my talk. The slides are up on slideshare, and here are the PDF slides. UPDATE: Gil Raphaelli has posted his python bindings he wrote for our libyahoo2 use in our Ops IM Bot. There was something that I left out of my slides, mostly because I didn’t want to distract from the main topic, wh
画像認識の初歩、SIFT,SURF特徴量
6. 画像特徴量の変遷 第一世代 第二世代 第三世代 対象物全体 局所情報 局所情報のつな 輝度分布 SIFT がり wavelet SURF Haar-like Joint Haar-like HOG Joint HOG Shapelet 1990年代 2000 - 2005 2006 - 現在 7. 局所特徴量 SIFT 1999 •Google の論文で言及http://www.esprockets. com/papers/www2008-jing-baluja.pdf。ただし、アルゴリズムが著作 権で守られている。C#ライブラリ libsift, OpenCV SURF 2006 SIFT の処理速度改良版。OpenCV haar-like特徴 2001 近接する2 つの矩形領域の明度差 Haar- 顔認識に用いられ(OpenCVの顔検出) like HOG(Histogram o
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
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
AWS vs GCP 今から作る ならどっちがいいの!?
Apache Avro in LivePerson [Hebrew]
IIS20131023
An Introduction to Accumulo
Should I Use Scalding or Scoobi or Scrunch?
http://polyglotprogramming.com/papers/WhyScalaIsTakingOverTheBigDataWorld.pdf
Spark at Twitter - Seattle Spark Meetup, April 2014
Real-time Recommendations using Spark Comcast Labs