Machine Learning by Example - Apache Spark
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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.
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Machine Learning by Example - Apache Spark
- 1. Service Symphony Ltd Apache Spark Machine Learning by Example Meeraj Kunnumpurath 25th of February 2017 1
- 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: [email protected] ❖ Presentation: https://www.slideshare.net/MeerajKunnumpurath/machine-learning-by- example-apache-spark ❖ GitHub: https://github.com/kunnum/sandbox/tree/master/notebooks 2
- 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 3
- 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 4
- 5. Apache Spark - Architecture 5
- 6. Apache Spark - Architecture 6
- 7. Apache Spark - Architecture 7
- 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 8
- 9. Spark - Scala Notebook 9
- 10. Spark - Scala Notebook 10
- 11. ML Components 11
- 12. ML Components ❖ Data Structures ❖ Vectors and Matrices ❖ Data Frames ❖ Feature Extractors and Transformers ❖ Estimators ❖ Models ❖ Pipelines ❖ Evaluators ❖ Tuning Aids 12
- 13. ML Components - Notebook 13
- 14. ML Components - Notebook 14
- 15. ML Components - Notebook 15
- 16. ML Components - Notebook 16
- 17. Spark ML - Pipeline Architecture ❖ Dataframe ❖ Estimator ❖ Transformer ❖ Pipeline ❖ Parameter 17
- 18. Spark ML - Pipeline Architecture 18 Training time flow Pipeline in estimator mode Pipeline.fit() Creates a pipeline model
- 19. Spark ML - Pipeline Architecture 19 Test time flow Pipeline in transformer mode PipelineModel.transform() Creates dataframe with augmented prediction columns
- 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 25
- 26. Regression 26
- 27. Linear Regression - Notebook 27
- 28. Linear Regression - Notebook 28
- 29. Linear Regression - Notebook 29
- 30. Linear Regression - Notebook 30
- 31. Linear Regression - Notebook 31
- 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 32
- 42. Clustering ❖ Unsupervised learning algorithm based on similarity vectors ❖ Multiple algorithms ❖ K-Means Clustering ❖ LDA - Latent Dirichlet Allocation ❖ Bisecting K-Means ❖ Gaussian Mixture Model 42
- 43. Clustering 43
- 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 52
- 54. Collaborative Filtering - Notebook 54
- 55. Collaborative Filtering - Notebook 55
- 56. Collaborative Filtering - Notebook 56
- 57. Collaborative Filtering - Notebook 57
- 58. Collaborative Filtering - Notebook 58
- 59. Collaborative Filtering - Notebook 59
- 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 60
- 61. Model Tuning - Notebook 61
- 62. Model Tuning - Notebook 62
- 63. Model Tuning - Notebook 63
- 64. Model Tuning - Notebook 64
- 65. Model Tuning - Notebook 65
- 66. Model Tuning - Notebook 66
- 67. Questions 67