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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Overview of ML algorithms

A large number of ML algorithms have been developed to date, and more are being researched and invented at an accelerated pace by academia and industry alike. This section will review some popular traditional and deep learning algorithms and how these algorithms can be applied to different kinds of ML problems. But first, let's quickly discuss the considerations when choosing an ML algorithm for the task.

Consideration for choosing ML algorithms

There are a number of considerations when it comes to choosing ML algorithms for different tasks:

Training data size: Some ML algorithms, such as deep learning algorithms, can work very well and produce highly accurate models, but they require large amounts of the training data. Traditional ML algorithms, such as linear models, can work effectively when the dataset is small but cannot take advantage of large datasets as effectively as deep learning neural network algorithms. Traditional ML algorithms...