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

Core features of open source machine learning libraries

At their core, machine learning libraries are just software libraries written in different programming languages. What makes them different from other software libraries are the functions they support. In general, most ML libraries have support for the following key features via different library sub-packages:

  • Data manipulation and processing: This includes support for different data tasks such as loading data of different formats, data manipulation, data analysis, data visualization, and data transformation.
  • Model building and training: This covers support for built-in machine learning algorithms as well as capabilities for building custom algorithms. Most ML libraries also have built-in support for the commonly used loss functions (such as mean squared error or cross-entropy) and a list of optimizers (such as gradient descent or adam) to choose from. Some libraries also provide advanced support for distributed model...