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Machine Learning Automation with TPOT

You're reading from   Machine Learning Automation with TPOT Build, validate, and deploy fully automated machine learning models with Python

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Product type Paperback
Published in May 2021
Publisher Packt
ISBN-13 9781800567887
Length 270 pages
Edition 1st Edition
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Author (1):
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Dario Radečić Dario Radečić
Author Profile Icon Dario Radečić
Dario Radečić
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Toc

Table of Contents (14) Chapters Close

Preface 1. Section 1: Introducing Machine Learning and the Idea of Automation
2. Chapter 1: Machine Learning and the Idea of Automation FREE CHAPTER 3. Section 2: TPOT – Practical Classification and Regression
4. Chapter 2: Deep Dive into TPOT 5. Chapter 3: Exploring Regression with TPOT 6. Chapter 4: Exploring Classification with TPOT 7. Chapter 5: Parallel Training with TPOT and Dask 8. Section 3: Advanced Examples and Neural Networks in TPOT
9. Chapter 6: Getting Started with Deep Learning: Crash Course in Neural Networks 10. Chapter 7: Neural Network Classifier with TPOT 11. Chapter 8: TPOT Model Deployment 12. Chapter 9: Using the Deployed TPOT Model in Production 13. Other Books You May Enjoy

Chapter 9: Using the Deployed TPOT Model in Production

You've made it to the final chapter – congratulations! So far, you've learned the basics of TPOT by solving classification and regression tasks, how TPOT works with Dask and neural networks, and how to deploy machine learning models both locally and to the cloud.

This chapter will serve as icing on the cake, as you'll learn how to communicate with your deployed models to build something even a 5-year-old could use. To be more precise, you'll learn how to communicate with your API through a notebook environment and a simple GUI web application.

This chapter will cover the following topics:

  • Making predictions in a notebook environment
  • Developing a simple GUI web application
  • Making predictions in a GUI environment
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