Are you feeling overwhelmed by AI and ML eating the world as a product manager? You’re not alone.
I was you, and I trained myself to be able to deliver value using hundreds of ML models in production, and manage a Data Science team, coming from a design background - this list is what helped me.
Almost 70% of companies interviewed in O’Reilly’s latest report on AI adoption are evaluating or have AI solutions in production. The report highlight that the most important bottleneck to adoption is the lack of talent, especially AI product managers.
Your next product is likely to be powered by data/models. Time to upgrade, buckle up, and get ready for the next product revolution.
- Paid Online Courses on AI and product management
- Books for AI Product Managers
- Free web resources on AI PM (guides, podcasts, Youtube)
1. Paid Online Courses to kickstart your learning journey
Online courses are great to get a basic understanding of the discipline, from frameworks to tactics, while getting proof of work with a certification that will shine on your resume or Linkedin profile.
I have completed all of these courses, and I can now say that not all systems are created equally—If you are serious about having a career in AI product management, I can’t recommend enough Duke University (Jon Reifschneider is the MVP).
- AI Product management by Duke University on Coursera
- Product Management for AI & Data Science by 365 Data Science on Udemy
- BUS 253 — Artificial Intelligence Bootcamp for Product and Business Managers by Stanford Contiuning Studies
- AI for everyone by Deeplearning.ai on Coursera
2. Books for AI Product managers
You’re more of a bookworm? I got you.
There are not that many options, I think Product Management for AI is an essential book for wannabe AI PMs, and Data Science for Business will help those who are more advanced.
I’ve also put a few books with a business and societal perspective, that will help you shape a vision of the future of AI and how it can affect your product.
- Product Management for AI by Justin Norman, Peter Skomoroch, Mike Loukides
- Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking by Foster Provost, Tom Fawcett
- Doing AI: A Business-Centric Examination of AI Culture, Goals, and Values by Richard Heimann
- The Master Algorithm by Pedro Domingos
- AI for People and Business by Alex Castrounis
- Prediction Machines: The Simple Economics of Artificial Intelligence by Ajay Agrawal, Joshua Gans, Avi Goldfarb
3. Additional free web resources to specialize yourself (Guides, podcasts, Youtube videos)
Now you’ve acquired essential skills, and a broad vision of how AI affects businesses, I encourage you to stay updated on other aspects that are super important: Best practices by MAANG companies, podcasts, and webinars through leaders.
- Rules of Machine Learning by Google Developers
Become a better machine learning engineer by following these machine learning best practices used at Google.
- People + AI Guidebook by People + AI Research (PAIR), Google
This guide assists UXers, PMs, and developers in collaboratively working through AI design topics and questions.
- Good Data Analytics by Google Developers
This guide describes the tricks that an expert data analyst uses to evaluate huge data sets in machine learning problems.
- Me, myself and AI by MITSolan and BCG
- Applied AI Pod by Alexandra Petrus
- AI, Machine Learning & Chatbots for Product Managers by Product School
Don’t forget, an AI product is still a product, a large majority of PM methodology, tactics and tools apply. But with a higher failure rate, big upfront investment on the line, and a lot of expectations—building specific AI PM skills seems reasonable 🙂
But since PM is at the confluence of multiple disciplines, you might what to go deeper.
Everybody will start their journey at a different starting point: If you come from a product background, it’s a great idea to get a little more technical and a better intuition on what’s possible. Starting with the Andrew Ng Machine Learning course or the data analytics certification by Google.
- Machine Learning Specialization by Andrew Ng/DeepLearning.ai on Coursera
- Data Analytics Professional Specialization by Google on Coursera
If you come from an engineering/data background, you could learn more about UX for AI and the business side. It will have a massive impact on adoption—both from your stakeholders and your end-users!
- UX for AI guides by Lennart Ziburski
- People + AI Research Library on Google Design
- AI and UX: Why Artificial Intelligence Needs User Experience by Gavin Lew and Robert M. Schumacher Jr.
What do you think about my list? Did I miss out on any cool tactics to learn AI PM? Let me know on Twitter.
And happy learning! 🙏