Dua / O'Neill / Boesch Machine Learning with Spark

Develop intelligent, distributed machine learning systems
2. Auflage 2025
ISBN: 978-1-78588-642-3
Verlag: De Gruyter
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 532 Seiten

Create scalable machine learning applications to power a modern data-driven business using Spark 2.xKey Features - [*] Get to the grips with the latest version of Apache Spark
- [*] Utilize Spark's machine learning library to implement predictive analytics
- [*] Leverage Spark’s powerful tools to load, analyze, clean, and transform your data
Book DescriptionThis book will teach you about popular machine learning algorithms and their implementation. You will learn how various machine learning concepts are implemented in the context of Spark ML. You will start by installing Spark in a single and multinode cluster. Next you'll see how to execute Scala and Python based programs for Spark ML. Then we will take a few datasets and go deeper into clustering, classification, and regression. Toward the end, we will also cover text processing using Spark ML. Once you have learned the concepts, they can be applied to implement algorithms in either green-field implementations or to migrate existing systems to this new platform. You can migrate from Mahout or Scikit to use Spark ML. By the end of this book, you will acquire the skills to leverage Spark's features to create your own scalable machine learning applications and power a modern data-driven business.What you will learn - [*] Get hands-on with the latest version of Spark ML
- [*] Create your first Spark program with Scala and Python
- [*] Set up and configure a development environment for Spark on your own computer, as well as on Amazon EC2
- [*] Access public machine learning datasets and use Spark to load, process, clean, and transform data
- [*] Use Spark s machine learning library to implement programs by utilizing well-known machine learning models
- [*] Deal with large-scale text data, including feature extraction and using text data as input to your machine learning models
- [*] Write Spark functions to evaluate the performance of your machine learning models
Who this book is forIf you have a basic knowledge of machine learning and want to implement various machine-learning concepts in the context of Spark ML, this book is for you. You should be well versed with the Scala and Python languages.

Autoren/Hrsg.

Dua, Rajdeep

O'Neill, Brian

Boesch, Stephen

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken

Über Autor(innen)

Dua Rajdeep :

Rajdeep Dua has over 18 years experience in the cloud and big data space. He has taught Spark and big data at some of the most prestigious tech schools in India: IIIT Hyderabad, ISB, IIIT Delhi, and Pune College of Engineering. He currently leads the developer relations team at Salesforce India. He has also presented BigQuery and Google App Engine at the W3C conference in Hyderabad. He led the developer relations teams at Google, VMware, and Microsoft, and has spoken at hundreds of other conferences on the cloud. Some of the other references to his work can be seen at Your Story and on ACM digital library. His contributions to the open source community relate to Docker, Kubernetes, Android, OpenStack, and Cloud Foundry.Ghotra Manpreet Singh :

Manpreet Singh Ghotra has more than 15 years experience in software development for both enterprise and big data software. He is currently working at Salesforce on developing a machine learning platform/APIs using open source libraries and frameworks such as Keras, Apache Spark, and TensorFlow. He has worked on various machine learning systems, including sentiment analysis, spam detection, and anomaly detection. He was part of the machine learning group at one of the largest online retailers in the world, working on transit time calculations using Apache Mahout, and the R recommendation system, again using Apache Mahout. With a master's and postgraduate degree in machine learning, he has contributed to, and worked for, the machine learning community.