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

Building intelligent solutions with AI services

AI services can be used for building different intelligent solutions. To determine if you can use an AI service for your use case, you must identify the business and ML requirements, then evaluate if an AI service offers the functional and non-functional capabilities you are looking for. In this section, we will present several business use cases and architecture patterns that incorporate AI services.

Automating loan document verification and data extraction

When we apply for a loan from a bank, we need to provide the bank with physical copies of documentation such as tax returns, pay stubs, bank statements, and photo IDs. Upon receiving those documents, the bank needs to verify these documents and enter the information from these documents into loan application systems for further processing. At the time of writing, many banks still perform this verification and data extraction process manually, which is time-consuming and error...