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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

Product type Paperback

Published in Dec 2022

Publisher Packt

ISBN-13 9781803237015

Length 382 pages

Edition 1st Edition

Tools

AWS

Concepts

Machine Learning

Authors (4):

Trenton Potgieter

Shreyas Subramanian

Farooq Sabir

Mani Khanuja

View More author details

Table of Contents (20) Chapters

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

Why subscribe?

19. Other Books You May Enjoy

Summary

In this chapter, we explored various data analysis methods, reviewed some of the AWS services for analyzing data, and launched a CloudFormation template to create an EMR cluster, SageMaker Studio domain, and other useful resources. We then did a deep dive into code for analyzing both structured and unstructured data and suggested a few methods for optimizing its performance. This will help you to prepare your data for training ML models.

In the next chapter, we will see how we can train large models on large amounts of data in a distributed fashion to speed up the training process.

The rest of the chapter is locked

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Authors (4)

Mani Khanuja

Mani Khanuja

Mani Khanuja is a seasoned IT professional with over 17 years of software engineering experience. She has successfully led machine learning and artificial intelligence projects in various domains, such as forecasting, computer vision, and natural language processing. At AWS, she helps customers to build, train, and deploy large machine learning models at scale. She also specializes in data preparation, distributed model training, performance optimization, machine learning at the edge, and automating the complete machine learning life cycle to build repeatable and scalable applications.

See other products by Mani Khanuja

Trenton Potgieter

Trenton Potgieter

Trenton Potgieter is an expert technologist with 25 years of both local and international experience across multiple aspects of an organization; from IT to sales, engineering, and consulting, on the cloud and on-premises. He has a proven ability to analyze, assess, recommend, and design appropriate solutions that meet key business criteria, as well as present and teach them from engineering to executive levels.

See other products by Trenton Potgieter

Shreyas Subramanian

Shreyas Subramanian

Shreyas Subramanian helps AWS customers build and fine-tune large-scale machine learning and deep learning models, and rearchitect solutions to help improve the security, scalability, and efficiency of machine learning platforms. He also specializes in setting up massively parallel distributed training, hyperparameter optimization, and reinforcement learning solutions, and provides reusable architecture templates to solve AI and optimization use cases.

See other products by Shreyas Subramanian

Farooq Sabir

Farooq Sabir

Farooq Sabir is a research and development expert in machine learning, data science, big data, predictive analytics, computer vision, and image and video processing. He has over 10 years of professional experience.

See other products by Farooq Sabir

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