Machine Learning for PMs

Uzma Barlaskar, Product Lead, Facebook

This comprehensive 3-part article series covers: 1) Problem Mapping: What types of problems are best suited for machine learning 2) ML Skills: What additional skills does a PM need to build products that leverage machine learning? 3) Caveats: What are the common mistakes made in building products that use machine learning

Inga Chen, Product Manager, Personalization, Spotify

Inga uses case studies from Squarespace and Automatic to show what problems are good for ML, how PMs help execute ML projects, and how to augment the standard product development lifecycle to build ML products.

Clemens Mewald, Product Manager, Google

Clemens covers the three different levels where ML can integrate with your product - making features better, making a new feature possible, or making a new product itself possible. He also covers an overview of tools in the ML toolbox - recommendation systems, event prediction, classification, generative models, and clustering. He wraps up with a case study on how ML is used in Google Forms.

Pete Skomoroch, Head of AI Automation & Data Products, Workday

Pete's presentation provides insight into how ML impacts product development and product management processes.