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MLbase: Distributed Machine Learning Made Easy

Posted on November 27, 2012 by Ameet Talwalkar

mlbase_logo2

Implementing and consuming Machine Learning techniques at scale are difficulttasks for ML Developers and End Users. MLbase is a platform addressing the issues of both groups, and consists of three components: MLlib, MLI, ML Optimizer.

For more details, please visit http://mlbase.org.

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Tags: Big Data, distributed machine learning


Projects

  • Akaros - An operating system for many-core architectures and large-scale SMP systems
  • Alluxio (formerly Tachyon), a Memory Speed Virtual Distributed Storage System
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  • MLbase: Distributed Machine Learning Made Easy
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  • Shark: SQL and Rich Analytics at Scale
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  • SparkNet
  • Sparrow: Low Latency Scheduling for Interactive Cluster Services
  • Splash: Efficient Stochastic Learning on Clusters
  • Succinct: Enabling Queries on Compressed Data
  • Velox: Models in Action

Tags

Akaros amp application Approximate Query Processing BDAS Best Paper Award Big Data BlinkDB Bootstrap cluster coflow consistency crowdsourcing databases Datacenters data centers Data Cleaning data quality Declarative ML distributed machine learning genomics Graphs hadoop Machine Learning Materialized Views matrix factorization mesos MLbase Optimization OS pbs PIQL query processing Sampling SCADS scalability scale independence scheduling Shark spark SQL storage Succinct transactions vldb

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