> For the complete documentation index, see [llms.txt](https://www.mlcompendium.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://www.mlcompendium.com/readme/introduction.md).

# Introduction

The AI Compendium was previously the Machine & Deep Learning Compendium, also known as the ML Compendium and the ML & DL Compendium. The old order is the order the notes were collected, and this order is the order of the work. An AI engineer solves a business problem with data, AI, and engineering, and puts the result in a product, so the model is one step on that path.

The first diagram shows the change: the notes used to run from types of learning to a separate ops book, and now they run from the business problem to the product.

```mermaid
flowchart TD
 subgraph collected [How the notes were collected]
 types[Types of learning]
 foundations[Foundations mixed with management]
 methods[Methods filed by folder]
 opsHalf[Ops as a second book]
 types --> foundations --> methods --> opsHalf
 end
 subgraph written [How the book is written]
 business[Business problem]
 data[Data]
 ai[AI]
 engineering[AI Engineering]
 product[AI Product]
 business --> data --> ai --> engineering --> product
 end
 collected --> written
```

The second diagram opens the Data step into its four parts, because most of the work between the business question and the model is there.

```mermaid
flowchart LR
 business[Business problem]
 datasets[Datasets]
 processing[Data Processing]
 analytics[Analytics]
 dataEng[Data Engineering]
 ai[AI]
 engineering[AI Engineering]
 product[AI Product]
 business --> datasets --> processing --> analytics --> dataEng --> ai --> engineering --> product
```

The notes inside each chapter are the sources for a later manuscript, and two posts explain where that manuscript comes from. [The ML & Ops Compendium Open Books](https://medium.com/data-science-collective/the-ml-ops-compendium-open-books-43a3c571d971) (December 2024) explains the open books this manuscript grows from. The earlier one, the August 2021 post titled The Machine & Deep Learning Compendium Open Book, is pinned on the [author profile](https://cohenori.medium.com/) and belongs in this introduction.


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