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Machine Learning in Biotechnology and Life Sciences

You're reading from   Machine Learning in Biotechnology and Life Sciences Build machine learning models using Python and deploy them on the cloud

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Product type Paperback
Published in Jan 2022
Publisher Packt
ISBN-13 9781801811910
Length 408 pages
Edition 1st Edition
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Author (1):
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Saleh Alkhalifa Saleh Alkhalifa
Author Profile Icon Saleh Alkhalifa
Saleh Alkhalifa
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Table of Contents (17) Chapters Close

Preface 1. Section 1: Getting Started with Data
2. Chapter 1: Introducing Machine Learning for Biotechnology FREE CHAPTER 3. Chapter 2: Introducing Python and the Command Line 4. Chapter 3: Getting Started with SQL and Relational Databases 5. Chapter 4: Visualizing Data with Python 6. Section 2: Developing and Training Models
7. Chapter 5: Understanding Machine Learning 8. Chapter 6: Unsupervised Machine Learning 9. Chapter 7: Supervised Machine Learning 10. Chapter 8: Understanding Deep Learning 11. Chapter 9: Natural Language Processing 12. Chapter 10: Exploring Time Series Analysis 13. Section 3: Deploying Models to Users
14. Chapter 11: Deploying Models with Flask Applications 15. Chapter 12: Deploying Applications to the Cloud 16. Other Books You May Enjoy

Chapter 12: Deploying Applications to the Cloud

In the previous chapter, we focused our efforts on integrating our models within the Flask framework to develop two main methods of serving data to end users: Graphical User Interfaces (GUIs), and Application Programming Interfaces (APIs). Using the Flask framework, we managed to locally deploy our models for development purposes only. In this chapter, we will take the next step forward and deploy our model to the cloud, thus making it available not only locally to ourselves but also across the web to many other users.

There are many different deployment platforms out there, such as Amazon Web Services (AWS), Google Cloud Platform (GCP), Azure, and Heroku, each of which serves to fulfill a number of needs. In each of these platforms, there are a number of solutions, each containing its respective pros and cons. For each of these solutions, there are a number of ways we can deploy a framework within them. Essentially, the number of...

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