Behind the Craft by Peter Yang

Behind the Craft by Peter Yang

Share this post

Behind the Craft by Peter Yang
Behind the Craft by Peter Yang
How to Break into AI Product Management | Marily Nika (Google)
Copy link
Facebook
Email
Notes
More
Podcast

How to Break into AI Product Management | Marily Nika (Google)

A crash course on AI algorithms and how to break into and succeed as an AI PM

Peter Yang's avatar
Peter Yang
Sep 22, 2024
∙ Paid
27

Share this post

Behind the Craft by Peter Yang
Behind the Craft by Peter Yang
How to Break into AI Product Management | Marily Nika (Google)
Copy link
Facebook
Email
Notes
More
2
Share

Dear subscribers,

Today, I want to share a new episode with Marily Nika.

Marily got her PhD in machine learning and spent 12 years building AI products at Meta and Google. She has also taught 5,000+ students through her highly-rated AI newsletter and course and given talks at TED, Harvard, and more.

Watch now on YouTube, Apple, and Spotify.

Marily and I talked about:

  • (00:00) How anyone can be an AI PM

  • (01:51) A crash course on AI algorithms and applications

  • (06:38) Traits that the best AI PMs have in common

  • (09:53) How to get leadership buy-in for AI products

  • (11:37) A day in the life as an AI PM

  • (14:51) How to break into AI product management

  • (20:25) How to avoid the retention problem for AI products

Read on for the interview takeaways.


A crash course on AI algorithms

Let's start with a quick explainer of some core AI concepts. What's your definition of AI, and how is it different from machine learning?

AI is about giving a computer intelligence to think and behave like a person.

Here’s a market map that shows all the different types of AI algorithms and applications. Let’s break it down:

Supervised learning

Supervised learning is like teaching an eager student with millions of examples. You show the computer lots of labeled data, and it learns to recognize patterns.

  1. Classification: This is the AI's way of sorting things into discrete categories. Example: Using millions of images to learn to distinguish cats from dogs.

  2. Regression: Predicting continuous values rather than categories given inputs. Example: Predicting a house’s sale price based on square feet, location, and more.

Unsupervised learning

Unsupervised learning is about finding patterns in data without being told what to look for. It's like giving a child a box of LEGOs and seeing what they build.

This post is for paid subscribers

Already a paid subscriber? Sign in
© 2025 Peter Yang
Privacy ∙ Terms ∙ Collection notice
Start writingGet the app
Substack is the home for great culture

Share

Copy link
Facebook
Email
Notes
More