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)
Section 1: Solving Business Challenges with Machine Learning Solution Architecture
Section 2: The Science, Tools, and Infrastructure Platform for Machine Learning
Section 3: Technical Architecture Design and Regulatory Considerations for Enterprise ML Platforms

Understanding the PyTorch deep learning library

PyTorch is an open source machine learning library that was designed for deep learning using GPUs and CPUs. It was released in 2016, and it is a highly popular machine learning framework with a large following and many adoptions. Many technology companies, including tech giants such as Facebook, Microsoft, and Airbnb, all use PyTorch heavily for a wide range of deep learning use cases, such as computer vision and natural language processing.

PyTorch strikes a good balance of performance (using a C++ backend) with ease of use with default support for dynamic computational graphs and interoperability with the rest of the Python ecosystem. For example, with PyTorch, you can easily convert between NumPy arrays and PyTorch tensors. To allow for easy backward propagation, PyTorch has built-in support for automatically computing gradients, a vital requirement for gradient-based model optimization.

The PyTorch library consists of several...