Book Image

Distributed Machine Learning with Python

By : Guanhua Wang
Book Image

Distributed Machine Learning with Python

By: Guanhua Wang

Overview of this book

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. With the help of this practical guide, you'll be able to put your Python development knowledge to work to get up and running with the implementation of distributed machine learning, including multi-node machine learning systems, in no time. You'll begin by exploring how distributed systems work in the machine learning area and how distributed machine learning is applied to state-of-the-art deep learning models. As you advance, you'll see how to use distributed systems to enhance machine learning model training and serving speed. You'll also get to grips with applying data parallel and model parallel approaches before optimizing the in-parallel model training and serving pipeline in local clusters or cloud environments. By the end of this book, you'll have gained the knowledge and skills needed to build and deploy an efficient data processing pipeline for machine learning model training and inference in a distributed manner.
Table of Contents (17 chapters)
Section 1 – Data Parallelism
Section 2 – Model Parallelism
Section 3 – Advanced Parallelism Paradigms


In this chapter, we mainly discussed a new method of distributed machine learning, called federated learning. The key concept of federated learning is that it enables collaborative model training without sharing each worker's local data. Thus, federated learning makes it possible for data privacy applications such as multiple banks to collaboratively train a model for fraud detection, for example.

After reading this chapter, you should understand how federated learning works via sharing knowledge without sharing real data. You should also understand how to use the TFF platform for federated learning. In addition, you should understand the concept of TinyML and its requirements. Finally, you should have learned how TensorFlow Lite satisfies all the requirements of TinyML.

In the next chapter, we will learn about elastic model training and serving.