What is ML Engineering?

A broad overview of the field, how it compares to software engineering, its relationship to AI, and a deepdive into how an ML-powered app works.

Heads up: I’m on my spring break, as per my holiday schedule. This means that this week and next week I’m publishing issues only on Tuesdays, and there is no Thursday, The Scoop issue. I’ll be back on reflecting on recent events with renewed energy after!

Q: “As a software engineer, I’m interested in machine learning (ML.) Could you give an overview of this field, and some basics worth knowing about?”

Machine learning is a hot topic and the popularity of this field is only growing, especially with the recent focus on large language models (LLMs) and the huge buzz about AI. It was just last week that we covered The productivity impact of AI coding tools.

For an overview of what machine learning is, I turned to Vicki Boykis, a longtime machine learning engineer, who’s been in the machine learning/data space for over a decade. She is currently a Senior Machine Learning Engineer at Duo Security, and was previously at Automattic (Tumblr, WordPress) and has worked as an ML consultant. Vicki writes a tech newsletter, a blog, and has organized a non-traditional and very interesting tech conference called Normcore Tech Conference, which took place last December. 

In this issue, Vicki covers:

1. Her background

2. What is machine learning?

3. A brief history of machine learning

4. How do ML projects work?

5. Machine learning and “Artificial Intelligence” (AI.) How do they relate?

6. A deep dive into how an ML-powered app works

7. What does the ML landscape look like, today?

With that, it’s over to Vicki:

1. My background