Scalable and Distributed Machine Learning and Deep Learning Patterns (Advances in Computational Intelligence and Robotics)

"By the end of this book, you will have the knowledge and abilities necessary to construct and implement a distributed data processing pipeline for machine learning model inference and training. Reduced time costs in machine learning result in shorter model training and model updating cycle wait times. Distributed machine learning enables ML professionals to reduce model training and inference time drastically. With the aid of this helpful manual, you'll be able to use your Python development experience and quickly get started with the creation of distributed ML, including multi-node ML systems"--

J. Joshua Thomas is a senior lecturer at KDU Penang University College, Malaysia since 2008. He obtained his PhD (Intelligent Systems Techniques) in 2015 from University Sains Malaysia, Penang, and Master's degree in 1999 from Madurai Kamaraj University, India. From July to September 2005, he worked as a research assistant at the Artificial Intelligence Lab in University Sains Malaysia. From March 2008 to March 2010, he worked as a research associate at the same University. Currently, he is working with Machine Learning, Big Data, Data Analytics, Deep Learning, specially targeting on Convolutional Neural Networks (CNN) and Bi-directional Recurrent Neural Networks (RNN) for image tagging with embedded natural language processing, End to end steering learning systems and GAN. His work involves experimental research with software prototypes and mathematical modelling and design He is an editorial board member for the Journal of Energy Optimization and Engineering (IJEOE), and invited guest editor for Journal of Visual Languages Communication (JVLC-Elsevier). He has published more than 30 papers in leading international conference proceedings and peer reviewed journals.