Breaking into AI Product Management

Help me launch a Cohort Based Course for AI/ML Product Managers!

TLDR: Help me build a course on AI PM by completing this short survey!

Transitions are never easy; career pivots even more so.

From being rejected at the recruiter stage because of "lack of experience," to fine-tuning your resume just one more time in hopes that someone would give you a chance, making a job switch especially during COVID times can feel like a daunting challenge. Feeling imposter syndrome and pigeon-holed into what your career has been so far is commonplace.

This was my story

I made the transition into product management ~1 year ago after doing data science and machine learning as an engineer at Uber's self driving car group (Uber ATG). Knowing that my personality and interests were more in building products and working cross-functionally across business, marketing, design, and engineering, I took up the challenge of making the switch to PM.

It wasn’t easy. Left and right I kept getting blocked at the earliest stages of the recruiting process. Thankfully, through a lot of preparation and adjustments, I was able to land my first PM jobs at C3 AI and SambaNova Systems.

While I was incredibly fortunate to have mentors and managers support me along the way, I wished there was more content and training for people like myself in not only breaking into the field but also thriving in it.

How is AI/ML Product Management different from regular PM?

This will eventually warrant an entirely separate series of posts, but in short, being an AI/ML product manager not only requires a technical foundation in the theory of machine learning but also an understanding of the nuances of building probabilistic, and intelligent products that are fundamentally rooted in data.

As such, there are whole lists of concerns that are unique to the field such as dealing with imbalances in labeled data, designing algorithms that are transparent and interpretable to the end user, making use of specialized hardware (i.e. GPUs), among many others.

So a natural question is how does one get this knowledge and experience? And once equipped, how does one translate that into actual products that users will love?

This along with many other questions drove me to create a cohort-based course (CBC) detailing my experience into doing AI Product Management.

What is a Cohort-Based Course?

A cohort-based course is an intentionally small group of students and instructors coming together to learn about a highly focused topic. They are live, in-person (or remote because of COVID), with a high emphasis on collaboration and group learning.

Unlike other online courses, each student begins the course at the same time and progresses through the course together, keeping one another accountable throughout. As a result of offering a higher quality experience, CBCs boast top-notch student completion rates.

From Wes Kao, co-founder of Maven:

The key difference between this phase of online ed and the MOOCs in the past decade? They are engaging and real-time, not just self-paced, and involve community-driven, active learning, as opposed to solo, passive content consumption. Cohort-based courses have a fixed start and end date, enforcing the real-time aspect and creating a scarcity within the abundance of content out there, and are often taught live. It’s the equivalent of participating in a college discussion seminar — taught by an expert in the field, unconstrained by geography or school rank — as opposed to watching a static video. And, importantly, there’s a built-in social contract in the form of the cohort. - In Online Ed, Content Is No Longer King—Cohorts Are

After being inspired by what I saw coming out from the instructors at Maven, I wanted to dive right in and apply what I knew in building a course for AI/ML Product Management.

Help me Build this Course!

As content creators know, focus is key.

Because product management is inherently so broad, it’s tough to know what exactly would be helpful for the course to hone in on.

That’s where I turn to you.

As part of the spirit of #buildinginpublic, I want to openly share my process in building out this CBC. It’ll be rough around the edges at first, but with your help, I hope this can become the best content out there on AI/ML Product Management.

If you could fill out my 3 minute survey, that would be amazing!

Let me know if this is interesting to you! Feel free to reach out on LinkedIn or Twitter. My DMs are open :)

P.S. If you’re interested in hearing more about my story, I recently did an interview with my friend Leon on his podcast.