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  • Get solid theoretical understandings of ML algorithms
  • Configure Spark on cluster and cloud infrastructure to develop applications using Scala, Java, Python, and R
  • Scale up ML applications on large cluster or cloud infrastructures
  • Use Spark ML and MLlib to develop ML pipelines with recommendation system, classification, regression, clustering, sentiment analysis, and dimensionality reduction
  • Handle large texts for developing ML applications with strong focus on feature engineering
  • Use Spark Streaming to develop ML applications for real-time streaming
  • Tune ML models with cross-validation, hyperparameters tuning and train split
  • Enhance ML models to make them adaptable for new data in dynamic and incremental environments

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.

  • Get the most up-to-date book on the market that focuses on design, engineering, and scalable solutions in machine learning with Spark 2.0.0
  • Use Spark’s machine learning library in a big data environment
  • You will learn how to develop high-value applications at scale with ease and a develop a personalized design
Page Count 476
Course Length 14 hours 16 minutes
ISBN 9781785888748
Date Of Publication 26 Oct 2016


Md. Rezaul Karim

Md. Rezaul Karim is a researcher, author, and data science enthusiast with a strong computer science background, coupled with 10 years of research and development experience in machine learning, deep learning, and data mining algorithms to solve emerging bioinformatics research problems by making them explainable. He is passionate about applied machine learning, knowledge graphs, and explainable artificial intelligence (XAI). Currently, he is working as a research scientist at Fraunhofer FIT, Germany. He is also a PhD candidate at RWTH Aachen University, Germany. Before joining FIT, he worked as a researcher at the Insight Centre for Data Analytics, Ireland. Previously, he worked as a lead software engineer at Samsung Electronics, Korea.

Md. Mahedi Kaysar

Md. Mahedi Kaysar is a Software Engineer and Researcher at the Insight Center for Data Analytics (the largest data analytics center across the Ireland and the largest semantic web research institute in the world), Dublin City University (DCU), Ireland. Before joining the Insight Center at DCU, he worked as a Software Engineer at the Insight Center for Data Analytics, National University of Ireland, Galway and Samsung Electronics, Bangladesh.

He has more than 5 years of experience in research and development with a strong background in algorithms and data structures concentrating on C, Java, Scala, and Python. He has lots of experience in enterprise application development and big data analytics.

He obtained a BSc in Computer Science and Engineering from the Chittagong University of Engineering and Technology, Bangladesh. Now, he has started his postgraduate research in Distributed and Parallel Computing at the Dublin City University, Ireland.

His research interests include Distributed Computing, Semantic Web, Linked Data, big data, Internet of Everything, and machine learning. Moreover, he was involved in a research project in collaboration with CISCO Systems Inc. in the area of Internet of Everything and Semantic Web Technologies. His duties were to develop an IoT-enabled meeting management system, a scalable system for stream processing, designing, and showcasing the use cases of a project.