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Data Science with .NET and Polyglot Notebooks

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

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Product type Paperback
Published in Aug 2024
Publisher Packt
ISBN-13 9781835882962
Length 404 pages
Edition 1st Edition
Languages
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Author (1):
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Matt Eland Matt Eland
Author Profile Icon Matt Eland
Matt Eland
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Table of Contents (22) Chapters Close

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 21. Other Books You May Enjoy

Adopting and Deploying Polyglot Notebooks

In this chapter, we’ll cover how Polyglot Notebooks can help you and your team succeed not just with exciting new machine learning tasks but also with the work you’re already doing. We’ll also cover how your notebooks can live outside of VS Code through hosting in a Jupyter notebook, JupyterLab, or GitHub codespace environment. Additionally, we’ll cover some practical career concerns with how to add Artificial Intelligence (AI) and machine learning to your day job – or prepare to transition to a role that supports this.

In this chapter, we’ll cover the following topics:

  • Integrating Polyglot Notebooks into your day job
  • Sharing Polyglot Notebooks with your team
  • Deploying Polyglot Notebooks to GitHub codespaces
  • Advancing into machine learning and AI
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