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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Summary

In this chapter, we saw how generative AI can involve using large pre-trained models to generate content from simple prompts.

We saw how chat completions allow you to generate text based on a prompt and conversation history, how image generation allows you to create serious and silly imagery, and how text embeddings help determine how similar or different the topics of two pieces of text are.

The models we interacted with in this chapter were all hosted on Azure in an Azure OpenAI Service, but generative AI can work with models directly on OpenAI, models hosted on another service (through a different set of APIs), or even models downloaded locally, using a library such as LLamaSharp. See the Further reading section for more information on these resources.

Working with generative AI does not require a notebook environment, but in this chapter, we saw how Notebooks can make the generative AI experience easier. Notebooks can specifically help you inspect complex result...