Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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

Arrow left icon
Product type Paperback
Published in Aug 2024
Publisher Packt
ISBN-13 9781835882962
Length 404 pages
Edition 1st Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Matt Eland Matt Eland
Author Profile Icon Matt Eland
Matt Eland
Arrow right icon
View More author details
Toc

Table of Contents (22) Chapters Close

Preface 1. Part 1: Data Analysis in Polyglot Notebooks FREE CHAPTER
2. Chapter 1: Data Science, Notebooks, and Kernels 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

Deploying generative AI models on Azure

The two best ways of interacting with readily available large language models are currently either through OpenAI directly or the Azure OpenAI Service.

Both services require you to provide an API key to identify yourself and receive charges from requests to the LLM that you wish to interact with.

Both services also offer a variety of different models to interact with. These models are periodically updated with minor changes. Additionally, the model catalog regularly expands as new models are trained, evaluated, and made available to the public.

While both of these services are viable ways of interacting with LLMs, this book will focus on Azure OpenAI Service. This is because Azure OpenAI Service tends to be a better option for enterprise users, due to its privacy, security, and content safety features.

Additionally, many organizations use Azure for other services, and there can be strong advantages to having your LLMs hosted in the...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image