Large Scale Machine Learning with Spark
Data processing, implementing related algorithms, tuning, scaling up and finally deploying are some crucial steps in the process of optimising any application.
Spark is capable of handling large-scale batch and streaming data to figure out when to cache data in memory and processing them up to 100 times faster than Hadoop-based MapReduce.This means predictive analytics can be applied to streaming and batch to develop complete machine learning (ML) applications a lot quicker, making Spark an ideal candidate for large data-intensive applications.
This book focuses on design engineering and scalable solutions using ML with Spark. First, you will learn how to install Spark with all new features from the latest Spark 2.0 release. Moving on, you’ll explore important concepts such as advanced feature engineering with RDD and Datasets. After studying developing and deploying applications, you will see how to use external libraries with Spark.
In summary, you will be able to develop complete and personalised ML applications from data collections,model building, tuning, and scaling up to deploying on a cluster or the cloud.
|Course Length||14 hours 16 minutes|
|Date Of Publication||26 Oct 2016|