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

16. Other Books You May Enjoy

What are AI services?

AI services are pre-built fully managed services that perform a particular set of ML tasks out of the box, such as facial analysis or text analysis. The primary target users for AI services are application developers who want to build AI applications without the need to build ML models from scratch. In contrast, the target audiences for ML platforms are data scientists and ML engineers, who need to go through the full ML life cycle to build and deploy ML models. For an organization, AI services mainly solve the following key challenges:

Lack of high-quality training data for ML model development: To train high-quality models, you need a large amount of high-quality curated data. For many organizations, data poses many challenges in data sourcing, data engineering, and data labeling.
Lack of data science skills for building and deploying custom ML models: Data science and ML engineering skills are scarce in the market and expensive to acquire.