AI Product Management

Product School, Try Exponent, and Product Allinace are good resources for PM interviews prep. 

Later is a good question. Interesting idea. I don't know of any, but it so interesting that someone should be offering it. Perhaps they might have rolled into certification or cohort courses with live projects!

Assuming you are specifically interested in AI product management, I would suggest these approaches to get started with ML. While I was new to ML/AI, these approaches helped me.

1. Online courses

2. Part-time certifications

3. Conferences

4. Kaggle

5. Publish your work

6. Internship/Volunteering

I covered this in my session on AI/ML Product Management: https://www.linkedin.com/video/event/urn:li:ugcPost:6929801568753971200/

If you are looking for guidance on general product management, let me know. There is a lot of material online and I can point you to.

Key traits for AI PM is no different from other PM roles -- empathy for customer issues, ability craft / create / articulate problems and how we might approach the solution, industry and domain experience, and collaborative leadership to work with engineering. Willingness to learn or prior experience or understanding of AI/ML modeling challenges, and how they can be use in the context the industry / domain where it is applied is ofcourse a big plus. 

AI PMs are expected to have a broad understanding of ML lifecycle and knowledge of key ML trends in the industry. Ensure your product management basics are strong: Deep focus on customers, business priority, foster an experimentation culture. In this session, I also covered key traits of successful AI product managers: https://www.linkedin.com/video/event/urn:li:ugcPost:6929801568753971200/. Hope this helps!

In addition to core business metrics that are improtant for a product success, below are the additional ones AI PMs obsess over to ensure the success upon launch doesn't regress over time.

1. Precision and Recall
2. False Positive
3. Model quality monitoring metrics based on where is the risk to business (feature quality, score shift, re-training frequency, etc)

AI/ML by definition requires a decent foundational understanding for AI/ML/Deep Learning Concepts, trends in industry, tools and methodologies to be able to work with engineering in defining solutions to customer /user problems. For the forseeable future, I would say that most AI/ML product managers would likely function as both technical as well as regular product managers to blend user journeys into concrete AL/ML solutions. 

PMs are generally categorised into B2C (Consumer), B2B (Enterprise), Platform, and Product. PMs role is generally 2 of these 4 things. Within this one can also be generalist PM vs domain PM vs growth PM vs scale PM etc. AI PM is the same. One additional categorey can be about building ML Ops platform but I am not convinced one needs a PM for it, or can't be fit into one of the 4 categories described above.

PMs working on Alexa can be AI PMs, but not necessarily TPMs.

First PM in a company! I have not done it, nor have anyone in close network to have a good understanding. My guess is that they have to establish right roles/responsibilities on what to carve out from the CEO. Perhaps focused on scaling up product for next million users (or take on next set of enteprise clients), or execution focsed. Do take this with a grain of salt as I am guessing based on when should a CEO hire their first PM.

Trust & Risk is a specific domain, so generalist PM would need to pivot to become a domain PM. This will require launching solutions across multiple years. Fraud evolves every year, but over 90% still remains the same decade long type. Payment Risk is a good stepping stone. Balacing security with usability (frictions to stop bad actors) which translates to doing a trade off between fraud loss and revenue loss is a new skill PMs have to develop.