You're reading from The Machine Learning Solutions Architect Handbook Create machine learning platforms to run solutions in an enterprise setting

Product type Paperback

Published in Jan 2022

Publisher Packt

ISBN-13 9781801072168

Length 442 pages

Edition 1st Edition

Languages

Python

Tools

TensorFlow

Concepts

Machine Learning

Author (1):

David Ping

View More author details

Table of Contents (17) Chapters

Preface

1. Section 1: Solving Business Challenges with Machine Learning Solution Architecture

2. Chapter 1: Machine Learning and Machine Learning Solutions Architecture FREE CHAPTER

3. Chapter 2: Business Use Cases for Machine Learning

4. Section 2: The Science, Tools, and Infrastructure Platform for Machine Learning

5. Chapter 3: Machine Learning Algorithms

6. Chapter 4: Data Management for Machine Learning

7. Chapter 5: Open Source Machine Learning Libraries

8. Chapter 6: Kubernetes Container Orchestration Infrastructure Management

9. Section 3: Technical Architecture Design and Regulatory Considerations for Enterprise ML Platforms

10. Chapter 7: Open Source Machine Learning Platforms

11. Chapter 8: Building a Data Science Environment Using AWS ML Services

12. Chapter 9: Building an Enterprise ML Architecture with AWS ML Services

13. Chapter 10: Advanced ML Engineering

14. Chapter 11: ML Governance, Bias, Explainability, and Privacy

15. Chapter 12: Building ML Solutions with AWS AI Services

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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.