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Applied Machine Learning and High-Performance Computing on AWS

You're reading from   Applied Machine Learning and High-Performance Computing on AWS Accelerate the development of machine learning applications following architectural best practices

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Product type Paperback
Published in Dec 2022
Publisher Packt
ISBN-13 9781803237015
Length 382 pages
Edition 1st Edition
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Authors (4):
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Trenton Potgieter Trenton Potgieter
Author Profile Icon Trenton Potgieter
Trenton Potgieter
Shreyas Subramanian Shreyas Subramanian
Author Profile Icon Shreyas Subramanian
Shreyas Subramanian
Farooq Sabir Farooq Sabir
Author Profile Icon Farooq Sabir
Farooq Sabir
Mani Khanuja Mani Khanuja
Author Profile Icon Mani Khanuja
Mani Khanuja
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Toc

Table of Contents (20) Chapters Close

Preface 1. Part 1: Introducing High-Performance Computing
2. Chapter 1: High-Performance Computing Fundamentals FREE CHAPTER 3. Chapter 2: Data Management and Transfer 4. Chapter 3: Compute and Networking 5. Chapter 4: Data Storage 6. Part 2: Applied Modeling
7. Chapter 5: Data Analysis 8. Chapter 6: Distributed Training of Machine Learning Models 9. Chapter 7: Deploying Machine Learning Models at Scale 10. Chapter 8: Optimizing and Managing Machine Learning Models for Edge Deployment 11. Chapter 9: Performance Optimization for Real-Time Inference 12. Chapter 10: Data Visualization 13. Part 3: Driving Innovation Across Industries
14. Chapter 11: Computational Fluid Dynamics 15. Chapter 12: Genomics 16. Chapter 13: Autonomous Vehicles 17. Chapter 14: Numerical Optimization 18. Index 19. Other Books You May Enjoy

Importance of data management

Data management is the process of effectively capturing, storing, and collating data created by different applications in your company to make sure it’s accurate, consistent, and available when needed. It includes developing policies and procedures for managing your end-to-end data life cycle. The following are some of the elements of the data life cycle specific to HPC applications, due to which it’s important to have data management policies in place:

  • Cleaning and transforming raw data to perform detailed faultless analysis.
  • Designing and building data pipelines to automatically transfer data from one system to another.
  • Extracting, Transforming, and Loading (ETL) data into appropriate data storage systems such as databases, data warehouses, and object storage or filesystems from disparate data sources.
  • Building data catalogs for storing metadata to make it easier to find and track the data lineage.
  • Following policies...
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