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

ML use cases in financial services

The Financial Services Industry (FSI), one of the most technologically savvy industries, is a front-runner in ML investment and adoption. Over the last several years, I have seen a wide range of ML solutions being adopted across different business functions within financial services. In capital markets, ML is being used in front, middle, and back offices to support investment decisions, trade optimization, risk management, and transaction settlement processing. In insurance, carriers are using ML to streamline underwriting, prevent fraud, and automate claim management. And banks are using ML to improve customer experience, combat fraud, and make loan approval decisions. Next, we will discuss several core business areas within financial services and how ML can be used to solve some of these business challenges.

Capital markets front office

In finance, the front office is the business area that directly generates revenue and mainly consists of...