Parallel Machine Learning

Parallelisation of ML Algorithms

AI, ML
ML System Optimization, MLSysOps, K-Means, Decision Trees, Random Forest, SVM, Parallel Machine Learning, AI, ML

Parallelisation of ML Algorithms #

Machine-learning algorithms expose different kinds of parallel work. The correct decomposition depends on whether independent work occurs across records, features, trees, clusters, kernel entries, or optimisation steps.

Course coverage:

  1. Problem decomposition for parallel machine learning
  2. Ensemble methods and XGBoost-style tree ensembles
  3. Parallel k-means assignment and centroid reduction
  4. Distributed decision trees and random forests
  5. Support vector machine parallelisation using block and MapReduce-style computation

Learning Objectives #

By the end of this page, you should be able to: