Distributed Machine Learning with PySpark

Abstract

Reducing time cost in machine learning leads to a shorter waiting time for model training and a faster model updating cycle. Distributed machine learning enables machine learning practitioners to shorten model training and inference time by orders of magnitude This book covers the following exciting features: Deploy distributed model training and serving pipelines Get to grips with the advanced features in TensorFlow and PyTorch Mitigate system bottlenecks during in-parallel model training and serving Discover the latest techniques on top of classical parallelism paradigm Explore advanced features in Megatron-LM and Mesh-TensorFlow Use state-of-the-art hardware such as NVLink, NVSwitch, and GPUs