Spring 2023 by Haiping Lu and Shuo Zhou, with Tahsin Khan and Mike Smith
In this module, we will learn how to do machine learning at large scale using Apache Spark. We will use the High Performance Computing (HPC) cluster systems of our university. If you are NOT on the University's network, you must use VPN (Virtual Private Network) to connect to the HPC.
This edition uses PySpark 3.3.1, the latest stable release of Spark (Oct 25, 2022), and has 10 sessions below. You can refer to the overview slides for more information, e.g. timetable and assessment information.
- Session 1: Introduction to Spark and HPC (Haiping Lu)
- Session 2: RDD, DataFrame, ML pipeline, & parallelization (Haiping Lu)
- Session 3: Scalable logistic regression (Shuo Zhou)
- Session 4: Scalable generalized linear models (Shuo Zhou)
- Session 5: Scalable matrix factorisation for collaborative filtering in recommender systems (Haiping Lu)
- Session 6: Scalable k-means clustering and Spark configuration (Haiping Lu)
- Session 7: Scalable PCA for dimensionality reduction and Spark data types (Haiping Lu)
- Session 8: Scalable decision trees and ensemble models (Tahsin Khan)
- Session 9: Apache Spark in the Cloud (Mike Smith)
- Session 10: Scalable neural networks (Tahsin Khan)
You can also download the Spring 2022 version for preview or reference.
If you do not have one yet, we recommend you to sign up for a GitHub account to learn using this popular open source software development platform.
Haiping and Shuo have recently developed a course on An Introduction to Transparent Machine Learning, part of the Alan Turing Institute’s online learning courses in responsible AI. If interested, you can refer to this introductory course with emphasis on transparency in machine learning to assist you in your learning of scalable machine learning.
The materials are built with references to the following sources:
- The official Apache Spark documentations. Note: the latest information is here.
- The PySpark tutorial by Wenqiang Feng with PDF - Learning Apache Spark with Python. Also see GitHub Project Page. Note: last update in Dec 2022.
- The Introduction to Apache Spark course by A. D. Joseph, University of California, Berkeley. Note: archived.
- The book Learning Spark: Lightning-Fast Data Analytics, 2nd Edition, O'Reilly by Jules S. Damji, Brooke Wenig, Tathagata Das & Denny Lee, with a github repository.
- The book Spark: The Definitive Guide by Bill Chambers and Matei Zaharia. There is also a Repository for code from the book.
Many thanks to
- Mauricio A Álvarez, who has co-developed this module with Haiping Lu from 2016 to 2022. His contributions are still reflected in the materials.
- Mike Croucher, Neil Lawrence, Will Furnass, Twin Karmakharm, and Vamsi Sai Turlapati for their inputs and inspirations since 2016.
- Our teaching assistants and students who have contributed in many ways since 2017.