Amirghodsi / Rajendran / Hall Apache Spark 2.x Machine Learning Cookbook

Over 100 recipes to simplify machine learning model implementations with Spark
1. Auflage 2025
ISBN: 978-1-78217-460-8
Verlag: De Gruyter
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 666 Seiten

No detailed description available for "Apache Spark 2.x Machine Learning Cookbook".

Autoren/Hrsg.

Amirghodsi, Siamak

Rajendran, Meenakshi

Hall, Broderick

Mei, Shuen

Über Autor(innen)

Amirghodsi Siamak :

Siamak Amirghodsi (Sammy) is interested in building advanced technical teams, executive management, Spark, Hadoop, big data analytics, AI, deep learning nets, TensorFlow, cognitive models, swarm algorithms, real-time streaming systems, quantum computing, financial risk management, trading signal discovery, econometrics, long-term financial cycles, IoT, blockchain, probabilistic graphical models, cryptography, and NLP.Guller Mohammed :

Author of Big Data Analytics with Spark - http://www.apress.com/9781484209653Mei Shuen :

Shuen Mei is a big data analytic platforms expert with 15+ years of experience in designing, building, and executing large-scale, enterprise-distributed financial systems with mission-critical low-latency requirements. He is certified in the Apache Spark, Cloudera Big Data platform, including Developer, Admin, and HBase. He is also a certified AWS solutions architect with emphasis on peta-byte range real-time data platform systems.Rajendran Meenakshi :

Meenakshi Rajendran is experienced in the end-to-end delivery of data analytics and data science products for leading financial institutions. Meenakshi holds a master's degree in business administration and is a certified PMP with over 13 years of experience in global software delivery environments. Her areas of research and interest are Apache Spark, cloud, regulatory data governance, machine learning, Cassandra, and managing global data teams at scale.Hall Broderick :

Broderick Hall is a hands-on big data analytics expert and holds a masters degree in computer science with 20 years of experience in designing and developing complex enterprise-wide software applications with real-time and regulatory requirements at a global scale. He is a deep learning early adopter and is currently working on a large-scale cloud-based data platform with deep learning net augmentation.