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Building machine learning applications locally with Spark — Joel Pinho Lucas (Tailtarget) @PAPIs Connect — São Paulo 2017


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In times of huge amounts of heterogeneous data available, processing and extracting knowledge requires more and more efforts on building complex software architectures. In this context, Apache Spark provides a powerful and efficient approach for large-scale data processing. This talk will briefly introduce a powerful machine learning library (MLlib) along with a general overview of the Spark framework, describing how to launch applications within a cluster. In this way, a demo will show how to simulate a Spark cluster in a local machine using images available on a Docker Hub public repository. In the end, another demo will show how to save time using unit tests for validating jobs before running them in a cluster.

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Building machine learning applications locally with Spark — Joel Pinho Lucas (Tailtarget) @PAPIs Connect — São Paulo 2017

  1. 1. Building Machine Learning applications locally with Spark 21/06/2017 Joel Pinho Lucas
  2. 2. Agenda • Problems and Motivation • Spark and MLlib overview • Launching applications in a Spark cluster • Simulating a Spark cluster using Docker • Demo: deploying a Spark cluster in a local machine • Unit tests for Spark jobs 2
  3. 3. 3 • How to setup a Spark cluster (infra + configuration)? • Test and/or Debug a Spark job • All team should have the same environment
  4. 4. 4 • Lightweight cluster • One machine • Same environment for all team • Deployed easily in any platform Run Spark Locally with docker
  5. 5. 5 • Easy to develop (API in Java, Scala, Python, R) • High Quality algorithms • Fast to run • Lazy evaluation • In memory Storage
  6. 6. 6 Spark Execution Model
  7. 7. Cluster Types • Standalone • Apache Mesos • HadoopYarn 7
  8. 8. 8 Starting a Cluster Manually Manually Submitting an Application
  9. 9. Choose your Docker Image (or build your own and share) 9
  10. 10. Some available Spark Docker Images 10 • • • • • • •
  11. 11. 11
  12. 12. Example to Run • MLlib's FP-Growth algorithm • Data from the digital publishing domain • Problem: to find frequent patterns from navigation profiles • Write results in MongoDB 12
  13. 13. The Dataset 13
  14. 14. Unit Testing using Spark Testing Base • Launched in Strata NYC 2015 by Holden Karau (and maintained by the community) • Supports unit tests in Java, Scala and Python 14
  15. 15. Q&A - Contact ‣ Linkedin: ‣ Email: 15