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Applied Machine Learning for Healthcare and Life Sciences using AWS

You're reading from   Applied Machine Learning for Healthcare and Life Sciences using AWS Transformational AI implementations for biotech, clinical, and healthcare organizations

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Product type Paperback
Published in Nov 2022
Publisher Packt
ISBN-13 9781804610213
Length 224 pages
Edition 1st Edition
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Author (1):
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Ujjwal Ratan Ujjwal Ratan
Author Profile Icon Ujjwal Ratan
Ujjwal Ratan
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Table of Contents (19) Chapters Close

Preface 1. Part 1: Introduction to Machine Learning on AWS
2. Chapter 1: Introducing Machine Learning and the AWS Machine Learning Stack FREE CHAPTER 3. Chapter 2: Exploring Key AWS Machine Learning Services for Healthcare and Life Sciences 4. Part 2: Machine Learning Applications in the Healthcare Industry
5. Chapter 3: Machine Learning for Patient Risk Stratification 6. Chapter 4: Using Machine Learning to Improve Operational Efficiency for Healthcare Providers 7. Chapter 5: Implementing Machine Learning for Healthcare Payors 8. Chapter 6: Implementing Machine Learning for Medical Devices and Radiology Images 9. Part 3: Machine Learning Applications in the Life Sciences Industry
10. Chapter 7: Applying Machine Learning to Genomics 11. Chapter 8: Applying Machine Learning to Molecular Data 12. Chapter 9: Applying Machine Learning to Clinical Trials and Pharmacovigilance 13. Chapter 10: Utilizing Machine Learning in the Pharmaceutical Supply Chain 14. Part 4: Challenges and the Future of AI in Healthcare and Life Sciences
15. Chapter 11: Understanding Common Industry Challenges and Solutions 16. Chapter 12: Understanding Current Industry Trends and Future Applications 17. Index 18. Other Books You May Enjoy

Introducing Amazon SageMaker Studio

Amazon SageMaker Studio is an IDE for ML that is completely web-based and fully managed by AWS resources in the background. It provides an intuitive interface to access ML tools to build, train, deploy, monitor, and debug your ML models. It also provides studio notebooks, which have a JupyterLab interface preinstalled with popular data science libraries that allow you to begin experimenting immediately upon getting access to the studio notebooks interface. These notebooks can be scaled up or down for CPUs or GPUs depending on the workloads you want to run on them and also provides terminal access for you to install and manage third-party libraries for local runs of your experiments. Once you are done experimenting locally, you can call multiple SageMaker jobs to scale out your experiments to larger datasets and workloads that can be horizontally scaled to multiple instances instead of a single notebook environment. For example, SageMaker Processing...

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