You're reading from Data Science with .NET and Polyglot Notebooks Programmer's guide to data science using ML.NET, OpenAI, and Semantic Kernel

Product type Paperback

Published in Aug 2024

Publisher Packt

ISBN-13 9781835882962

Length 404 pages

Edition 1st Edition

Languages

Tools

Codespaces

Concepts

Artificial Intelligence

Author (1):

Matt Eland

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Table of Contents (22) Chapters

Preface

1. Part 1: Data Analysis in Polyglot Notebooks

2. Chapter 1: Data Science, Notebooks, and Kernels FREE CHAPTER

3. Chapter 2: Exploring Polyglot Notebooks

4. Chapter 3: Getting Data and Code into Your Notebooks

5. Chapter 4: Working with Tabular Data and DataFrames

6. Chapter 5: Visualizing Data

7. Chapter 6: Variable Correlations

8. Part 2: Machine Learning with Polyglot Notebooks and ML.NET

9. Chapter 7: Classification Experiments with ML.NET AutoML

10. Chapter 8: Regression Experiments with ML.NET AutoML

11. Chapter 9: Beyond AutoML: Pipelines, Trainers, and Transforms

12. Chapter 10: Deploying Machine Learning Models

13. Part 3: Exploring Generative AI with Polyglot Notebooks

14. Chapter 11: Generative AI in Polyglot Notebooks

15. Chapter 12: AI Orchestration with Semantic Kernel

16. Part 4: Polyglot Notebooks in the Enterprise

17. Chapter 13: Enriching Documentation with Mermaid Diagrams

18. Chapter 14: Extending Polyglot Notebooks

19. Chapter 15: Adopting and Deploying Polyglot Notebooks

20. Index

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Hosting ML.NET models in ASP.NET web applications

ML.NET models can be embedded into a wide range of applications, including Azure functions, console, desktop, and mobile applications. However, the most commonplace ML.NET models are deployed is to ASP.NET applications.

ASP.NET is a web application framework that allows developers to build web applications and web APIs. At the time of writing this chapter, builtwith.com estimated that there were over five million active websites running ASP.NET worldwide.

While covering the way ASP.NET works is beyond the scope of this book, there are a large number of resources available that cover ASP.NET in more detail, including a number of books from Packt.

You can embed an ML.NET model into an ASP.NET application by following these steps:

Copy the model’s .zip file into either the ASP.NET application’s root directory or to an online location that can be referenced via a URL.
Add classes to your project representing...