# Introduction

Here I'm collecting some of my thoughts around how to get better at building machine learning based products and services.

I think this is still a somewhat new area and we're collectively still figuring out how to do this. By collecting my thoughts here I am hoping to help others who are facing similar challenges.

## My background

I've personally worked on many of the aspects I think are relevant for doing machine learning "in practice".

* Originally, I started computer science and some of my favorite topics are programming languages and distributed system design.
* I hold a Ph.D. in machine learning and worked in applied and basic machine learning research for more than 10 years.
* I led teams building machine learning projects for several years.
* I have worked in a individual contributor role (a.k.a. principal or staff engineer) with machine learning teams. I did this from a department level up to some topics on a company level.
* I've recently started to consult companies around the topic.

For some reason, I'm seldom content with learning one thing well, but once that happens I'm always asking myself "but what now." This has taken me from fundamental research to the industry. I also have a hard time keeping myself to look beyond whatever scope I am in to figure out what the real solution to a problem is. I think both of these habits have driven me to look into the big picture when it comes to ML in practice and I hope that helps you, too.

I currently expect that this will rather become a collection of essays than a self-contained book.

## How to get in touch with me

Always happy to hear from you, you can either find me on [Twitter](https://twitter.com/mikiobraun) or [Linkedin](https://www.linkedin.com/in/mikiobraun/).


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://book.mlinpractice.com/master.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.