Distributed Machine Learning with Python
Distributed Machine Learning with Python takes you through state-of-the-art techniques built on top of traditional data and model parallelism approaches. It explains the concept of hybrid data-model parallelism, federated learning, and edge device learning with elastic and in-parallel model training in multi-tenant clusters.
Offered by
Difficulty Level
Intermediate
Completion Time
9h28m
Language
English
About Book
Who Is This Book For?
This book is for data scientists, machine learning engineers, and ML practitioners in both academia and industry. A fundamental understanding of machine learning concepts and working knowledge of Python programming is assumed. Prior experience implementing ML/DL models with TensorFlow or PyTorch will be beneficial. You'll find this book useful if you are interested in using distributed systems to boost machine learning model training and serving speed.
Distributed Machine Learning with Python
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 9h28m total length
Splitting Input Data
Parameter Server and All-Reduce
Building a Data Parallel Training and Serving Pipeline
Bottlenecks and Solutions
Splitting the Model
Pipeline Input and Layer Split
Implementing Model Parallel Training and Serving Workflows
Achieving Higher Throughput and Lower Latency
A Hybrid of Data and Model Parallelism
Federated Learning and Edge Devices
Elastic Model Training and Serving
Advanced Techniques for Further Speed-Ups
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