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The Machine Learning Solutions Architect Handbook

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

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Product type Paperback
Published in Jan 2022
Publisher Packt
ISBN-13 9781801072168
Length 442 pages
Edition 1st Edition
Languages
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Author (1):
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David Ping David Ping
Author Profile Icon David Ping
David Ping
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Toc

Table of Contents (17) Chapters Close

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.
  • ...
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