This document provides an agenda for a presentation on using machine learning with Apache Spark. The presentation introduces Apache Spark and its architecture, Scala notebooks in Spark, machine learning components in Spark, pipelines for machine learning tasks, and examples of regression, classification, clustering, collaborative filtering, and model tuning in Spark. It aims to provide a hands-on, example-driven introduction to applying machine learning using Apache Spark without deep dives into Spark architecture or algorithms.

1. Service Symphony Ltd
Apache Spark
Machine Learning by Example
Meeraj Kunnumpurath
25th of February 2017
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2. Introduction
❖ Working as technologist and software architect for couple of decades, at number of leading
financial institutions in the UK
❖ Authored a number books on Enterprise Java, Web Services and SOA
❖ Spoken at a number of technology conferences
❖ Founded Service Symphony Ltd in 2009 serving leading financial services customers
building mission critical middleware
❖ Engineer with a keen interest in ML, AI and Data Science
❖ Blog: http://www.servicesymphony.com/blog
❖ Email: meeraj@servicesymphony.com
❖ Presentation: https://www.slideshare.net/MeerajKunnumpurath/machine-learning-by-
example-apache-spark
❖ GitHub: https://github.com/kunnum/sandbox/tree/master/notebooks
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3. Agenda
❖ Introduction to using ML with Apache Spark
❖ Hands-on example driven approach
❖ Not a deep dive into Apache Spark Architecture
❖ Neither a deep dive into ML algorithms
❖ Examples built using Apache Zeppelin
❖ Some of the examples are from Spark ASF
documentation
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4. Apache Spark - Overview
❖ Open source large scale distributed data processing fabric
❖ Offers multiple components addressing different facets of data science for big and
fast data processing, ML, analytics and data ingestion
❖ Ability to process large amount of data in memory spanning multiple process
spaces
❖ Initially started as a research project in UC Berkeley
❖ Originally released under BSD, top level ASF project licensed under ASL 2.0 since
2014
❖ One of the most active open source project, arguably the most active ASF project
❖ Adopted, extended and commercialised by multiple vendors playing in the data
science realm
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5. Apache Spark - Architecture
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6. Apache Spark - Architecture
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7. Apache Spark - Architecture
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8. Scala - Spark Natural Transition
❖ Interest in Spark stemmed from deep interest in Scala
and functional programming
❖ Data processing echo system built around Scala, with a
strong synergy in Scala’s design motivations
❖ Extends Scala’s idiomatic functional programming
model to transcend beyond process boundaries
❖ Spark RDDs - Scala collections on steroids
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9. Spark - Scala Notebook
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10. Spark - Scala Notebook
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11. ML Components
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12. ML Components
❖ Data Structures
❖ Vectors and Matrices
❖ Data Frames
❖ Feature Extractors and Transformers
❖ Estimators
❖ Models
❖ Pipelines
❖ Evaluators
❖ Tuning Aids
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13. ML Components - Notebook
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14. ML Components - Notebook
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15. ML Components - Notebook
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16. ML Components - Notebook
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17. Spark ML - Pipeline Architecture
❖ Dataframe
❖ Estimator
❖ Transformer
❖ Pipeline
❖ Parameter
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18. Spark ML - Pipeline Architecture
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Training time flow
Pipeline in estimator mode
Pipeline.fit()
Creates a pipeline model

19. Spark ML - Pipeline Architecture
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Test time flow
Pipeline in transformer mode
PipelineModel.transform()
Creates dataframe with augmented prediction columns

20. ML Pipeline Notebook
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21. ML Pipeline Notebook
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22. ML Pipeline Notebook
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23. ML Pipeline Notebook
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24. ML Pipeline Notebook
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25. Regression
❖ Supervised Learning Algorithm for predicting continuous labels
❖ Multiple Algorithms
❖ Linear Regression
❖ Generalised Linear Regression
❖ Decision Tree Regression
❖ Random Forest Regression
❖ Gradient Boosted Tree Regression
❖ Survival Regression
❖ Isotonic Regression
❖ Works with input feature vectors and labelled points
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26. Regression
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27. Linear Regression - Notebook
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28. Linear Regression - Notebook
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29. Linear Regression - Notebook
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30. Linear Regression - Notebook
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31. Linear Regression - Notebook
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32. Classification
❖ Supervised learning for predicting discrete labels
❖ Multiple algorithms
❖ Binomial and polynomial logistic regression
❖ Decision tree classifier
❖ Random forest classifier
❖ Gradient boosted tree classifier
❖ Multi-layer neural network classifier
❖ Naive Bayes Classifier
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33. Classification
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34. Classification - Notebook
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35. Classification - Notebook
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36. Classification - Notebook
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37. Classification - Notebook
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38. Classification - Notebook
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39. Classification - Notebook
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40. Classification - Notebook
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41. Classification - Notebook
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42. Clustering
❖ Unsupervised learning algorithm based on similarity
vectors
❖ Multiple algorithms
❖ K-Means Clustering
❖ LDA - Latent Dirichlet Allocation
❖ Bisecting K-Means
❖ Gaussian Mixture Model
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43. Clustering
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44. Clustering - Notebook
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45. Clustering - Notebook
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46. Clustering - Notebook
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47. Clustering - Notebook
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48. Clustering - Notebook
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49. Clustering - Notebook
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50. Clustering - Notebook
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51. Clustering - Notebook
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52. Collaborative Filtering
❖ Commonly used for recommender systems
❖ Uses ALS (Alternating Least Squares) to learn latent
factors in user to item association
❖ Default assumption is based on explicit feedback for
matrix factorization
❖ You an explicitly enable implicit preferences
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53. Collaborative Filtering
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54. Collaborative Filtering - Notebook
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55. Collaborative Filtering - Notebook
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56. Collaborative Filtering - Notebook
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57. Collaborative Filtering - Notebook
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58. Collaborative Filtering - Notebook
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59. Collaborative Filtering - Notebook
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60. Model Tuning
❖ API to tune an individual estimator or the entire
pipeline using a normalised parameter model
❖ API to support k-fold cross validation
❖ API to evaluate performance on linear regression, as
well as binomial and polynomial classification
❖ API for performing training validation split
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61. Model Tuning - Notebook
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62. Model Tuning - Notebook
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63. Model Tuning - Notebook
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64. Model Tuning - Notebook
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65. Model Tuning - Notebook
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66. Model Tuning - Notebook
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67. Questions
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