Book Image

The Machine Learning Solutions Architect Handbook

By : David Ping
Book Image

The Machine Learning Solutions Architect Handbook

By: David Ping

Overview of this book

When equipped with a highly scalable machine learning (ML) platform, organizations can quickly scale the delivery of ML products for faster business value realization. There is a huge demand for skilled ML solutions architects in different industries, and this handbook will help you master the design patterns, architectural considerations, and the latest technology insights you’ll need to become one. You’ll start by understanding ML fundamentals and how ML can be applied to solve real-world business problems. Once you've explored a few leading problem-solving ML algorithms, this book will help you tackle data management and get the most out of ML libraries such as TensorFlow and PyTorch. Using open source technology such as Kubernetes/Kubeflow to build a data science environment and ML pipelines will be covered next, before moving on to building an enterprise ML architecture using Amazon Web Services (AWS). You’ll also learn about security and governance considerations, advanced ML engineering techniques, and how to apply bias detection, explainability, and privacy in ML model development. By the end of this book, you’ll be able to design and build an ML platform to support common use cases and architecture patterns like a true professional.
Table of Contents (17 chapters)
1
Section 1: Solving Business Challenges with Machine Learning Solution Architecture
4
Section 2: The Science, Tools, and Infrastructure Platform for Machine Learning
9
Section 3: Technical Architecture Design and Regulatory Considerations for Enterprise ML Platforms

Hands-on lab – running distributed model training with PyTorch

In this hands-on lab, you will use SageMaker Training Service to run data parallel distributed training. We will use PyTorch's torch.nn.parallel.DistributedDataParallel API as the distributed training framework and run the training job on a small cluster. We will reuse the dataset and training scripts from the hands-on lab in Chapter 8, Building a Data Science Environment Using AWS Services.

All right, let's get started!

Modifying the training script

First, we need to add distributed training support to the training script. To start, create a copy of the train.py file, rename the file train-dis.py, and open the train-dis.py file. You will need to make changes to the following three main functions. The following steps are meant to highlight the key changes needed. To run the lab, you can download the modified train-dis.py file from https://github.com/PacktPublishing/The-Machine-Learning-Solutions...