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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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Toc

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

Summary

Over the course of this chapter, we reviewed a number of ways to deploy our application to end users in the cloud. First, we explored the use of AWS Lightsail, which allowed us to deploy our code in the form of an online web application using a Docker container. Next, we explored the use of GCP's App Engine to deploy our code, once again in the form of an online web application, using its user-friendly and abstract methodologies. Finally, we deployed our code in the form of a repository to GitHub, allowing us to expose the content to users, professionals, and tentative employers alike.

Congratulations! With this last tutorial now complete, we have come to the end of this book. Looking back at the last 12 chapters, we have covered many different topics in a diverse set of areas. In the beginning, we learned some new languages such as Python and Structured Query Language (SQL) and used them to analyze and visualize our data. We then explored some of the most common ML...

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