Learn PySpark. Build Python-based Machine Learning and Deep Learning Models

Description

Leverage machine and deep learning models to build applications on real-time data using PySpark. This book is perfect for those who want to learn to use this language to perform exploratory data analysis and solve an array of business challenges. You'll start by reviewing PySpark fundamentals, such as Spark’s core architecture, and see how to use PySpark for big data processing like data ingestion, cleaning, and transformations techniques. This is followed by building workflows for analyzing streaming data using PySpark and a comparison of various streaming platforms. You'll then see how to schedule different spark jobs using Airflow with PySpark and book examine tuning machine and deep learning models for real-time predictions. This book concludes with a discussion on graph frames and performing network analysis using graph algorithms in PySpark. All the code presented in the book will be available in Python scripts on Github. What You'll LearnDevelop pipelines for streaming data processing using PySpark Build Machine Learning & Deep Learning models using PySpark latest offerings Use graph analytics using PySpark Create Sequence Embeddings from Text data Who This Book is For Data Scientists, machine learning and deep learning engineers who want to learn and use PySpark for real time analysis on streaming data. Front Matter ....Pages i-xviii Introduction to Spark (Pramod Singh)....Pages 1-16 Data Processing (Pramod Singh)....Pages 17-48 Spark Structured Streaming (Pramod Singh)....Pages 49-65 Airflow (Pramod Singh)....Pages 67-84 MLlib: Machine Learning Library (Pramod Singh)....Pages 85-115 Supervised Machine Learning (Pramod Singh)....Pages 117-159 Unsupervised Machine Learning (Pramod Singh)....Pages 161-181 Deep Learning Using PySpark (Pramod Singh)....Pages 183-203 Back Matter ....Pages 205-210