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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Model training environment

Within an enterprise, a model training environment is a controlled environment with well-defined processes and policies on how it is used and who can use them. Normally, it should be an automated environment that's managed by an MLOps team, though it can be self-service enabled for direct usage by data scientists.

Automated model training and tuning are the core capabilities of the model training environment. To support a broad range of use cases, a model training environment needs to support different ML and deep learning frameworks, training patterns (such as single-node and distributed training), and hardware (different CPUs and GPUs).

The model training environment manages the life cycle of the model training process. This can include authentication and authorization, infrastructure provisioning, data movement, data preprocessing, ML library deployment, training loop management and monitoring, model persistence and registry, training job management...