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The Machine Learning Solutions Architect Handbook

You're reading from  The Machine Learning Solutions Architect Handbook

Product type Book
Published in Jan 2022
Publisher Packt
ISBN-13 9781801072168
Pages 442 pages
Edition 1st Edition
Languages
Author (1):
David Ping David Ping
Profile icon David Ping

Table of Contents (17) Chapters

Preface 1. Section 1: Solving Business Challenges with Machine Learning Solution Architecture
2. Chapter 1: Machine Learning and Machine Learning Solutions Architecture 3. Chapter 2: Business Use Cases for Machine Learning 4. Section 2: The Science, Tools, and Infrastructure Platform for Machine Learning
5. Chapter 3: Machine Learning Algorithms 6. Chapter 4: Data Management for Machine Learning 7. Chapter 5: Open Source Machine Learning Libraries 8. Chapter 6: Kubernetes Container Orchestration Infrastructure Management 9. Section 3: Technical Architecture Design and Regulatory Considerations for Enterprise ML Platforms
10. Chapter 7: Open Source Machine Learning Platforms 11. Chapter 8: Building a Data Science Environment Using AWS ML Services 12. Chapter 9: Building an Enterprise ML Architecture with AWS ML Services 13. Chapter 10: Advanced ML Engineering 14. Chapter 11: ML Governance, Bias, Explainability, and Privacy 15. Chapter 12: Building ML Solutions with AWS AI Services 16. Other Books You May Enjoy

Understanding the Apache Spark ML machine learning library

Apache Spark is a distributed data processing framework for large-scale data processing. It allows Spark-based applications to load and process data across a cluster of distributed machines in memory to speed up the processing time.

A Spark cluster consists of a master node and worker nodes for running different Spark applications. Each application that runs in a Spark cluster has a driver program and its own set of processes, which are coordinated by the SparkSession object in the driver program. The SparkSession object in the driver program connects to a cluster manager (for example, Mesos, YARN, Kubernetes, or Spark's standalone cluster manager), which is responsible for allocating resources in the cluster for the Spark application. Specifically, the cluster manager acquires resources on worker nodes called executors to run computations and store data for the Spark application. Executors are configured with resources...

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