- eBook:Learn PySpark: Build Python-based Machine Learning and Deep Learning Models
- Author:Pramod Singh
- Edition:1st ed. edition
- Data:September 28, 2019
- Pages:210 pages
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 Learn
- Develop 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
Data Scientists, machine learning and deep learning engineers who want to learn and use PySpark for real time analysis on streaming data.
Chapter 2: Data Processing
Chapter 3: Spark Structured Streaming
Chapter 4: Airflow
Chapter 5: MLlib: Machine Learning Library
Chapter 6: Supervised Machine Learning
Chapter 7: Unsupervised Machine Learning
Chapter 8: Deep Learning Using PySpark