How to Become an AI Product Manager - AI PM Community Session #42

Summary

The video script discusses the high demand for AI talent and the importance of AI product management (AIPM) skills. Vian, founder of PM exercises, introduces their AIPM learning program designed to equip product managers with AI expertise. The program includes ongoing education, community access, and practical tools. Guest speaker Kai, from Uber's AI platform team, shares insights on AI's practical applications and the significance of product management in translating AI visions into reality. The presentation highlights the need for a deep understanding of AI technologies and the opportunity to network with influential figures in the AI space.

Takeaways

• 😀 The demand for AI talent is high, with many enterprise clients struggling to fund it, indicating the growing importance of AI in the industry.
• 🚀 Vian, the founder of PM exercises, emphasizes the mission to help people become better product managers, especially in the realm of AI product management.
• 🌟 The company initially focused on interview preparation but has expanded to ongoing education, recognizing the rapid changes in AI and the need for continuous learning.
• 🔑 Success stories from PM exercises' platform highlight the effectiveness of their approach in helping individuals prepare for AI product manager roles.
• 📈 Ongoing access to learning materials for three years is offered to ensure that skills remain up-to-date with the evolving AI landscape.
• 🛠️ PM exercises is focused on creating the best AI product manager learning program, being the first to establish a dedicated cohort for this complex field.
• 🤝 The program provides access to a community, ongoing learning, and an AI assistant to increase productivity, as well as opportunities to connect with influential figures in AI.
• 🌱 The transition from 'AI product managers' to just 'product managers' is anticipated, as understanding AI is expected to become a core competency for all product managers.
• 💼 The importance of not just getting hired but also excelling in the role is underscored, with the need for a deep technical understanding to manage AI products effectively.
• 🔍 The script discusses the process of identifying and creating job opportunities, with an emphasis on self-sourcing and the power of hackathons to build networks and skills.
• 📚 The content of the AI product management cohort is outlined, detailing the skills required and the structure of the program to develop a comprehensive understanding of AI in product management.

Q & A

What is the primary focus of PM exercises?

The primary focus of PM exercises is to help people become better product managers, with a special emphasis on AI product management as the next big thing for the PM community.

What is the significance of the statement 'today we call them AI product managers, but tomorrow we're just going to call them product managers'?

This statement signifies the belief that understanding AI will become a fundamental expectation for all product managers in the future, making the 'AI' prefix unnecessary.

How does PM exercises measure success in helping users prepare for interviews?

PM exercises measures success by tracking the success rate in landing jobs among people who have used their platform to prepare for interviews.

What is the importance of ongoing education in the AI product management field according to the transcript?

Ongoing education is crucial because the AI space is rapidly changing, and taking a course or cohort now might not be sufficient to keep one's skill set updated a year later.

What does PM exercises provide to users for ongoing learning?

PM exercises provides access to everything developed on an ongoing basis for a three-year period, ensuring that users' knowledge and skills in AI product management stay updated.

Who is Kai and what is his role in the context of the transcript?

Kai is the lead product manager for the AI platform team at Uber, managing Uber's internal machine learning platform called Michelangelo. He is involved in leading the AI product management cohort.

What is the role of hackathons in creating opportunities for AI product managers?

Hackathons are important for creating opportunities as they allow individuals to meet others interested in sharing knowledge, build products, and demonstrate thought leadership, making candidates more attractive for AI product management roles.

Why is it necessary to have a deep technical understanding as an AI product manager?

A deep technical understanding is necessary to apply product management skills effectively, make informed trade-offs, and turn visions or ideas into real AI products that solve real-world problems.

What are some of the hidden opportunities in the AI industry for product managers?

Hidden opportunities include positions that are not advertised and may only become available when a company identifies a perfect candidate for a problem they were not clear how to address.

How can an AI product manager candidate make themselves attractive to potential employers?

Candidates can make themselves attractive by gaining relevant experience, participating in hackathons, publishing thought leadership content, and demonstrating passion and willingness to learn and adapt in the AI space.

What is the importance of the interview evaluation process in securing a job as an AI product manager?

The interview evaluation process is crucial as it is where candidates often fall short. It is not enough to just get an introduction or referral; candidates must pass a rigorous interview process to demonstrate their AI product management skills and knowledge.

Outlines

00:00

🤖 Introduction to AI Product Management and PM Exercises

The speaker, Ban, introduces himself as the founder of PM Exercises, a company focused on helping individuals become better product managers. He discusses the company's mission, which is not centered on profitability but on educational value. Ban highlights the increasing importance of AI in product management and the demand for AI talent. He mentions the success of their platform in helping people prepare for interviews and the ongoing education they provide, which is crucial given the rapidly changing AI landscape. The speaker also introduces Kai, who will be leading the AI product management cohort, and shares his excitement about networking with influential people in the AI space.

05:00

🚀 The Evolution of AI and the Role of Product Managers

Kai, the lead product manager for Uber's AI platform, discusses the evolution of AI from a data science and engineering task to a field with practical applications that can change the world. He emphasizes the importance of product managers in turning AI visions into reality, especially with the abundance of capital and resources available. Kai also talks about the challenges of delivering on promises made by companies in the AI space and the necessity for product managers to have a deep technical understanding to succeed.

10:00

💼 Navigating the AI Job Market and the Importance of Self-Sourcing

The speaker discusses the strategies for landing a job in the AI field, noting that traditional methods such as submitting resumes or getting referrals are not enough. Instead, he suggests becoming an interesting candidate by self-sourcing opportunities, leveraging referrals, and showcasing a strong track record. The speaker shares personal anecdotes of how he created opportunities for himself through outreach and emphasizes the importance of being proactive and taking risks to stand out in the job market.

15:03

🛠 Building Expertise and Crafting a Compelling AI Narrative

The speaker advises on how to build expertise in AI and become an attractive candidate for AI product management roles. He suggests participating in hackathons, attending events, and publishing content to demonstrate thought leadership. The speaker also highlights the importance of being able to articulate a strong story during interviews, which can be achieved by building products, practicing skills, and engaging in intellectual conversations about technology.

20:04

📈 The AI Product Manager Learning Program and Community Support

The speaker outlines the AI product manager learning program offered by PM Exercises, which includes access to learning materials, experts, and a community of AI product managers. He discusses the importance of ongoing learning in a rapidly evolving field and the benefits of being part of a community that provides support, feedback, and job opportunities. The speaker also mentions the hands-on nature of the program, which includes team-based workshops and product building.

25:05

🔍 Deep Dive into AI Product Management Curriculum and Expectations

Kai provides an overview of the curriculum for the AI product management cohort, which covers AI and ML technical foundations, problem definition, data strategy, MOps, and responsible AI. He explains that the program is designed to help participants develop the necessary skills to become successful AI product managers, including understanding AI models, managing the model lifecycle, and ensuring responsible AI practices. The curriculum is structured to build a comprehensive PRD and includes live workshops and practical applications.

30:06

📚 Personal Support and Community Engagement in the Cohort Program

The speaker discusses the personal support available to participants in the cohort program, including access to the instructors' calendars for one-on-one advice. He emphasizes the importance of community engagement through a forum where questions can be posted and answered. The speaker also mentions the availability of learning materials for three years, ensuring that participants stay updated with the latest developments in the AI field.

35:07

🗓 Overview of the Cohort Structure and Application Process

The speaker provides details about the structure of the cohort, including the number of weeks, the content covered each week, and the bonus materials provided. He explains that the program is designed to build an end-to-end PRD and that participants will have the opportunity to present their product ideas. The speaker also shares the application process, the start date of the next cohort, and encourages interested individuals to apply.

40:09

📝 Final Thoughts and Closing Remarks

In the closing remarks, the speaker thanks the participants for attending the session and expresses excitement for the next cohort. He invites questions and offers to share the presentation and learning materials afterward. The speaker also provides his contact information for further inquiries related to the cohort and encourages participants to reach out for personal support.

Keywords

💡AI Talent

AI Talent refers to individuals with expertise in artificial intelligence, a field that is in high demand due to its growing importance in various industries. In the video, the founder of PM exercises discusses the struggle of enterprise clients to find and fund AI talent, highlighting the importance of this workforce for the advancement of AI technologies.

💡Product Manager (PM)

A Product Manager is responsible for guiding the development of a product from conception to launch and beyond. They are key in bridging the gap between users, business goals, and technical teams. The video emphasizes the need for product managers to have a deep understanding of AI, as it is becoming an integral part of their role.

💡AI Product Managers

AI Product Managers are specialized product managers who have a deep understanding of AI technologies and how they can be applied to create products. The video discusses the evolution of this role, suggesting that in the future, all product managers will need to have this expertise, as AI becomes a standard component of product development.

💡Ongoing Education

Ongoing Education refers to continuous learning and development, which is crucial in the fast-paced field of AI. The video mentions that PM exercises provides ongoing education to help individuals not only prepare for interviews but also to continually improve their skills as product managers, especially in the context of AI product management.

💡AI Platform

An AI Platform is a comprehensive set of tools, frameworks, and services that facilitate the development and deployment of AI applications. In the script, Kai, the lead product manager for Uber's AI platform, discusses the importance of such platforms in enabling companies to leverage AI for various use cases, including pricing, promotions, and fraud detection.

💡MLOps

MLOps is a practice for managing the lifecycle of machine learning systems, from development to deployment. It is a critical skill for AI Product Managers, as discussed in the video, because it involves understanding how to collect data, train models, and ensure the ongoing performance and monitoring of AI systems.

💡Hackathons

Hackathons are events where people, often programmers, collaborate intensively on a project. They are highlighted in the video as valuable opportunities for individuals to gain practical experience in AI, meet like-minded professionals, and demonstrate their skills and passion for the field.

💡

💡Evaluation Process

The Evaluation Process in the context of the video refers to the steps companies take to assess a candidate's suitability for a role, particularly in AI product management. It involves rigorous interviews and tests of technical and product management skills, which are crucial for candidates to pass in order to secure a job.

💡Technical Understanding

Technical Understanding is the comprehension of the underlying technologies and their applications. The video emphasizes the necessity for AI Product Managers to have a deep technical understanding of AI, as it enables them to make informed decisions and effectively manage AI projects.

💡Community

In the video, the term Community refers to a group of like-minded individuals who share knowledge and support each other's growth. The PM exercises platform offers access to a community of AI product managers, which provides opportunities for networking, learning from guest speakers, and engaging in team-based workshops.

💡PRD (Product Requirements Document)

A Product Requirements Document is a detailed description of a product's objectives, features, and technical specifications. The video mentions that participants in the PM exercises program will work on creating a PRD as part of their learning experience, which is a critical skill for product managers to articulate their vision to engineering teams.

Highlights

High demand for AI talent is causing many enterprise clients to struggle with funding.

PM Exercises, founded by Ban, aims to help people become better product managers with a focus on AI.

AI Product Managers are expected to have a deep understanding of AI, which will become a standard for all product managers.

PM Exercises provides ongoing education for AI Product Managers and has seen success in job placements.

Access to the PM Exercises platform includes a community, ongoing learning, and an AI assistant for three years.

Ban emphasizes the importance of having the best AI product manager learning program.

Kai, the lead product manager for Uber's AI platform, shares his experience and passion for growing the AI PM community.

The AI industry has shifted from theoretical discussions to practical applications, impacting various fields.

Product managers play a crucial role in turning AI visions into real products.

Hidden PM opportunities in the AI industry are often not advertised and require a proactive approach.

Referrals and self-sourcing are effective methods for uncovering unadvertised job opportunities.

Hackathons are a powerful way for aspiring AI PMs to gain experience and network.

Passing the evaluation process is key to landing a job, not just getting referrals or submitting resumes.

AI PMs need to demonstrate both traditional PM skills and AI-specific knowledge during interviews.

Building a strong story and showcasing AI expertise can help candidates stand out in interviews.

The PM Exercises AI PM learning program includes hands-on workshops and team-based product building.

Cohort participants gain access to a community of AI PMs and opportunities for personal support.

The program covers AI technical foundations, ML problem definition, data strategy, MOps, and responsible AI.

By the end of the cohort, participants aim to have a complete PRD ready for an engineering team.

The cohort includes optional content and recordings for deeper learning, requiring a time commitment of 5-15 hours.

The next cohort starts in May, with a structure that includes foundational knowledge and bonus materials.

Transcripts

[Music]

% of their Enterprise clients are

struggling with funding AI talent and

that really speaks to the high demand

that's out there so um first of all I'll

introduce myself I'm I'm ban founder of

PM exercises I we've been in business

for about six years now and uh since the

beginning it was a kind of a mission

driven uh company that is not really

optimizing for profitability or anything

like that it's optimizing for uh helping

people become better product managers it

really started from my own uh set of

experiences that I had in the industry

um trying to prepare for interviews so

that was what we were focused on for the

first few years and uh with AI we

decided this is kind of the next big

thing uh for the PM Community um I think

that we're going to enter a world where

um just like communication skills having

a deep understanding of how AI works is

going to be one of the expectations for

any product manager in any job and uh

today we call them AI product managers

but tomorrow we're just going to call

them product managers and we have some

good stats on the success in bending

jobs among people that have used our

platform to prepare for their interviews

um we we provide ongoing education this

really applies to a couple different

things not only that um we have a lot of

users who go through our platform use

our platform to prepare for interviews

but they also use it on an ongoing basis

to become better PMS uh but we actually

do do this also on the AI product

management side in fact you get access

to everything uh for a few years

afterwards um going through the cohort

and the reason for it is we understand

um there's a lot that's happening in the

space things are changing a lot and um

you taking a course or cohort now um

might not be sufficient uh for your

skill sets to be updated like a year

later right so anything that we share

and develop on an ongoing basis will be

available to you for threeyear period uh

and right now PM exercis is really

focused on um having the best AI product

manager learning program out there um I

can say with confident we were the first

ones in the world um that decided this

is so complex that needs its own cohort

and um we've had five successful

learning programs so far but now we're

expanding it um when you're part of the

program you get things like you know

access to a community ongoing learning

you get access to an AI assistant that

helps you become more productive uh but

uh what I'm also very excited about is

um the opportunity for me to get to meet

um other people other very influential

people in the AI space uh for example

Kai is one of them um I've been very

lucky and honored to get to know him um

I want to actually pass it to him uh do

his own intro and then I I'll come back

and continue so go ahead Kai sure thanks

Vian he folks uh my name is Kai I'm

currently the lead product manager for

the AI platform team here Uber I manage

the Uber's internal N2 machine learning

platform called mangel that offs you

know 100% of Uber's machine use cases

these are these are the use case such as

you know rider rider pricing uh driver

incentives promotions rer driver Mion is

delivery uh ETA uh map CTA and also

things like our East home feed

recommendations all the way to customer

services and also fraud detection so it

covers both the traditional machine

learning and also the recent Genera a

use cases at Uber um I will be leading

this cohort uh aipm cohort uh and uh

joined by a x aipm from Google Corey uh

I don't think Corey is here today right

no yeah it's not on the call

okay um why am I here I am super

passionate about the AI ml space and I

do want to help grow the AI mlpm Comm

Community because I think we're still at

a very early stage for uh for this for

this field and uh I just love sharing I

want to share my past experience some of

My Success a lot of my failures to help

you jumpstart your aipm career so

welcome I mean nice to meet all of you

guys back to you all right thank you so

much so um let's talk a little bit about

um what we're going to um go over and I

think this is very interesting uh slide

that um I think it kind of captures

what's really happening in the industry

especially in the world of AI um for a

long time um this was more of a um data

science or um engineering task um this

was like driven by engineering and data

scientists they were doing a lot of

research uh trying to kind of figure out

um you know how do you actually have um

some sort of a Technology stock that

delivers um at least artificial

intelligence in some like basic forms

shapes and form and one could argue that

um you know rule based systems that we

have been working with over the past 20

years maybe there were like kind of the

initial versions of it but um AI in the

form that we understand today uh was not

really available to us um until very

recently and about four or five years

ago things started shifting um there was

kind of more of

focus on talking about Ai and really

talking about the potential because

people could start imagining how Ai and

ml is going to change the world but um

given the technology wasn't there

initially this Focus was mostly on the

marketing side so um a lot of companies

got funded um lot of Visions were

communicated people could start

imagining how things are going to work

but a lot of them started u kind of not

delivering on their promises um until

about um a couple couple years ago when

um the world was kind of shocked at how

Ai and ml can actually start having

practical applications and this is when

I'm talking about the world I'm talking

about kind of people outside um the AI

ml community and um all of a sudden you

had a product um that basically

showcased how um Ai and ml could

actually solve real world problems

whether it's education healthare um

image detection and all these other

things and um with the rise of geni and

chat gbt a lot of companies actually

started getting also funded because they

could start imagining even more use

cases and um in a world where um there's

kind of abundance of funds available

abundance of capital and um there is

like an abundance of uh engineering

resources and marketing resources uh

what becomes important is um delivering

on those promises and like really

turning those Visions into reality and

uh who does that better than anybody

else is product managers right like you

need to kind of apply product management

skills to uh turn a vision or an idea

into a real product um do the tradeoffs

and in order for you to do that you have

to have really deep technical

understandings of how that part works

and um that's what we're all about at PM

exercises really kind of building that

foundation so um what we're going to

talk about today to kind of see how that

um you know could translate into an

opportunity for us at a very high level

is uh what are some of the Hidden PM

opportunities from an artificial

intelligent industry perspective um I'll

talk a little bit about um you know why

I'm talking about hidden and like what I

mean by that um the other part is um if

you come across as an opportunity um is

that sufficient right like you know if

you get intros introduced to another

company or um you know you send your res

uh is that sufficient um reality is

that's not sufficient because um there

are many other

uh competitors uh who are also aiming

for the same gole so um not only that um

you have to kind of make yourself look

attractive to them uh so that they're

willing to kind of pick up the phone or

like send send you an email to have a

conversation you have to be able to also

pass the interview and the evaluation

process right uh and we'll talk a little

bit about um the AI product management

learning um later um during this session

so this is what we're going to cover

cover at a very high level and um what's

interesting is that uh typically um we

think of like getting hired funnel as

you send your resume or you get a

referral and you get hired right um but

the reality is that's not the case this

is not how things are done even though

uh we continue thinking um maybe in our

subconscious level that this is how um

you can land a job this is not the

reality you just getting submitting your

resume um or you know getting a referral

um does not mean that um you've had

you're going to land the job and um the

best way for me to kind of um you know I

think make you kind of realize this as a

fact is to like think back about the

last time you were actually able to land

a job this way right or or do you know

anybody um who's ever landed a job this

way and probably the answer is no right

um just because you get a referral or

you get a resume um sent you're not

going to land your job you're not going

to get hired um the right way to do it

is somehow you become an interesting

candidate date um that comes under their

radar and there's so many ways for us to

do this resume is only one of them right

um but more important is for you to pass

the evaluation step right um and guess

what any company that's good um that's

really trying to build a meaningful team

in the space of AI and ml they're going

to make sure that they're doing a pretty

good job in hiring people um on the AI

ml side and what it means is that um

they're going to make they're going to

be very careful with their hiring

process they're going to be very

selective they're going to make you go

through a rigorous um hire interview

process um and um that's usually where

we fall short this is where we usually

fail so the only way for you to really

land the job is for you to pass that

evaluation process and um we're going to

talk a little bit about U what that

means over the next uh few slides I

think this is going to be pretty helpful

for um the existing uh cohort members on

the alumni and also like people that are

uh currently um evaluating um whether or

not they want to be part of the next

learning program on AI product

management

so the first thing that we're gonna kind

of ask ourselves is okay um how do I

come across as an opportunity right um

for me to actually even be an

interesting candidate um in the eyes of

um the potential hiring manager right

and the reality is that um there are a

lot of positions out there right that

you can apply for um but the real

opportunities are not the ones that are

advertised um they're the ones that

nobody knows about maybe the company

hasn't even decided um that they're

going to hire an individual and um they

would only like get that uh position

available once they see that oh there's

somebody who if they come on board um

they would be perfect for this problem

that they were dealing with and they

were not clear how to address right and

um there are a couple different ways to

kind of go about um coming across this

opportunity I think one of those

opportunities is um getting referrals

yes that's very important this doesn't

mean that you're going to actually land

a job that's very helpful um I've

basically received lot of offers from um

friends or um other um connections to my

previous um employers staff because

somebody said oh ban came in did this

like you should talk to them right

that's a great way way and that usually

means that you get that third party

approval that makes things easier but

this referral model works really well

when you have a strong track record

right you have a strong track record

then it makes sense because they're like

oh we want you because we heard you've

done a great job um in this particular

area in this fi right and the other part

is self Source right and when I say self

Source I'll kind of give you an example

on how you can self it self Source these

opportunities kind of create these

opportunities yourself and um once you

create those opportunities uh you still

have to basically make sure that you

pass the evaluation phase right uh so I

got like a couple examples of like you

know how um these opportunities could

exist in the real world uh this is like

a kind of a real example of my situation

right like I sent out an email to

somebody and I'm like hey I'm very

interested in um you know working with

your company and um I would like to

learn more about you and guess what they

replied back and

um we ended up actually talking and um I

ended up doing a Consulting gig for them

for a while I was very interested in

their technology and um here's another

example um this turned into a full-time

opportunity um I reached out basically a

company that I was very interested in uh

I told them about I heard about you guys

um I would like to know if there's a

chance for me to be part of your team I

just heard you guys raised money uh and

they didn't even have an opening right

and the CEO emailed back and said yes

I'm very interested in being part of

your team um so um I'm very interested

in you joining our team they invited me

I flew down to San Francisco and next

thing you know I join them as a

full-time staff so I think this is kind

of an example of how I create

opportunities for myself it's something

that definitely works and um the way I

do it the way I come across them is I

know what I'm interested in um maybe I

use like Google alert to be notified

about every single uh content that's

generated in my area of Interest I spend

a lot of time on like you know link

or um X or any other platform or like

popular Tech related sites to be

informed of any developments in that

space and if I see something I just

reach out to them right and you know

I've also not listed a lot of examples

of situations where I didn't get a

response right so um it's very important

for us to kind of realize it's not

guaranteed to be a success but it just

shows that this method works and you

have to kind of be willing to uh put

yourself out there um and reach out so

that they start evaluating you and I

want to emphasize and say this doesn't

mean that you're going to get the job it

just means that now you've soled the

very first step which is okay this is an

interesting candidate and we're willing

to evaluate that right so I kind of like

you know a couple things to highlight um

how does cold approaches work first of

all um you don't have to have um a job

experience in that particular area you

need to have an experience right um and

it's very important for us to realize

this ESP especially in emerging um

Industries like artificial intelligence

because um a lot of you who are thinking

about breaking into AI you're probably

thinking in your head I don't have a

full-time AI product manager experience

but that's okay um you can actually

create that um by becoming experts

spending time to read on things that you

think are important um generating

content that represents you have some

sort of a thought leadership and your

experience and knowledge has reached a

point where um you can actually have

your own independent opinion and you

have other ways for you

to gain that experience and one thing

that I'll highlight throughout this

presentation is hackathons like the

power of hackathons you going into

hackathons where you're going to meet a

bunch of other people who are actually

very interested in sharing their

knowledge with you like how great is

that like they're actually passionate

about finding somebody who's willing to

listen to them U and is willing to learn

from them and they're very happy to do

that and what this will do is um over

time it'll make you more attractive as a

candidate um it'll help you kind of

develop a deeper understanding of what

different companies do um particularly

the company that you're aiming for right

um and this will allow you also be more

selective on the type of companies that

you want to go after right it also shows

that you're passionate right it shows

that uh you actually are interested in

this not just because of the salary job

you're not going to be a 9 to5 type of a

person you're going to be somebody U

who's actually very interested even once

you land your job you're going to

continue um developing your skill set

and your knowledge maybe outside the the

working hours and that you know that's

exactly what an employer wants right um

and you also want to kind of show that

you're willing to make sacrifices when

you're entering a new space um in an

area where um you don't have necessarily

a lot of full-time experience and what I

mean by sacrificing is for example

sometimes it means making a sacrifice on

a title sometimes it means making a

sacrifice on your salary because let's

face it they're taking a risk on you and

um if you really want to kind of

maximize on your compensation uh you're

better off working in an area where you

already have a lot of expertise and

you're kind of like CCH capturing all

the fruits that are um already grown

right so whereas here you're kind of

entering into a new space um given that

they're taking a risk on you um there's

going to be a discounted price you can

think of think of it

um on your services and you have to be

realistic and accept that um in the hope

that in the long run right you will

become an attractive candidate to more

companies and eventually you become an

expert in the field that everybody wants

so that's something for you to kind of

keep in mind like you know when does

Whole approaches work well um before I

send those emails I already went to a

lot of hackathons I already attended a

lot of events I published a um my own

paper on a particular topic that I was

in interested in I could kind of refer

to it uh it made them see that I'm a

thought leader um and I I told them I

just want to be part of your team I

don't care what I'm going to be doing

right uh and that was very important

so now the question is going to become

you know how do you pass the um

evaluation process right uh and the

reality is um on a Surface um it sounds

like they basically want you to have all

these um great

attributes um that sometimes are kind of

conflicting with each other right for

example oh you're it's a new space like

how can you expect um all your hires to

have a lot of um you know experience in

this field right until three years ago

um maybe the number of people that were

working as AI product managers were

three digits so that's a reality and

there you know many other kind of

examples like this and that you know we

as PMS would probably experience but

this doesn't mean that um there's no um

there's no opportunity so there's a

couple ways for you to kind of become

that attractive candidate um one like I

mentioned um really becoming an

aipm um before um you land a job right

and the way you do that is by practicing

those set of skill sets wherever you get

a chance right um through um community

events or like hackathons um and

building products like there's nothing

more important than actually building

something that you can present and you

can kind of talk about because when you

actually build a product um that's when

you start realizing some of the

limitations of the technology so you can

have an intelligent conversation um it's

somebody actually mentioned to me once

that people that do well in interviews

um they think of a job interview as um

an intellectual conversation between two

smart people and I thought it was very

interesting U especially when you're

aiming for a company that's very

attractive um to a large number of

intelligent people that's good way of

thinking about it and the best way for

you to handle that conversation uh is

for you to be able to talk intelligently

about uh kind of the nuances of the tech

stack and the best way for you to learn

that is through building things right

like building a PRD um and if you're if

it's possible especially with no code

tools or um attending like a hackathon

even turning that into some sort of a

prototype um that allows you to um kind

of see those like limitations in the

real world uh and then writing about it

there's nothing like um writing when it

comes to learning it's very hard uh it's

very hard to be authentic and um good

news is that in the world of a ml um

there's so many areas um that are kind

of just like kind of scratching the

surface right now you can kind of uh go

really uh deep in one particular area U

we had somebody that actually went

really deep on one particular metric for

one particular company and he published

a um interesting blog post about it and

after the blog post was published uh he

started receiving a lot of contacts a

lot of interest from people on LinkedIn

like recruiter saying oh we really like

this blog post that you published we'

like to talk to you uh but it needs to

be genuinely um written by you and

should represent that you actually have

your independent thoughts and um once

you kind of reach that level um then

you're actually able to um see yourself

um available for many opportunities and

this is going to be a marathon more than

a Sprint that's the reality but it is

possible so then how do you actually Ace

the interview um I think there's a

couple things that it's really important

for us to realize is um you know there's

like the kind of the traditional PM

skills that are very relevant like you

know knowing how to do product sense and

how to do uh strategy questions and

behavioral you have to continue U being

able to pass those kinds of questions

and PM exercises has a lot of good

examples but you also need to have a

very AI specific knowledge right um and

this really comes down to you

understanding uh what's important for an

AI product manager right for example how

does an AI product manager get involved

on the data strategy side or how much

does an AI product manager get involved

on mlops uh because you're G to have to

be able to have uh a meaningful

conversation with some of your future

colleagues and some of those future

colleagues are you know the data

scientists and ml Engineers right um

here's the good news you can actually um

simulate those conversations before the

interviews and you can do that like I

said whether through like a physical

event like hackathon or through your um

you know personal contacts uh attending

online communities or physical physical

communities or events uh but that's what

you need to do um in order for you to be

able to have a comfortable conversation

uh with these people because

uh people like data scientists and ml

Engineers are very likely or at least

their um their criteria for a good

hiring process is very likely to be part

of the interview process right um so

what you have to do is kind of like you

know practice for that um and make sure

that you can you can do well and you

need to have a very strong story to

share and that strong story um ideally

if it's very attractive it will actually

take most of the interview time and

you'll be able to kind of um Define the

rules of the game during the interview

instead of letting them kind of take you

in any direction that they like uh and

like kind of trying to find a gotcha

moment right um and the best way to do

that is to like I said um build

something that you find interesting

that's probably better than um investing

even in a learning program and um you

still need to kind of practice a lot um

it's interesting to me like I've seen

people that I've landed um great jobs at

top tech companies uh like for example

Google and Facebook uh many of them just

on the mock interview side they've done

over 100 mock interviews um that means

over 100 hours right of course that's

not the case with everybody um but it's

important for us to be aware of and

couple notes here for the cohort U

members on on cohort 5 um if you guys

are interested um if you're kind of

practicing for your PM aipm interviews

uh feel free to reach out um to me or

Audrey and um I'll be happy to uh we'll

be happy to match you guys with um some

of the other uh alumni of the previous

cohorts who are also practicing for

their interviews um that's one of the

benefits of the community you guys can

practice and I kind of improve help each

other grow and um get better in your

interviews and the other thing that

we'll be doing is that um we will be

also U providing you with um a um the

the cohort five people um with an aipm

interview guide um sometime next week

this is um almost ready we're very

excited about sharing it with you and we

will also share this with some of the um

Al with the alumni of the previous

cohorts as well so hopefully that'll

help you guys U become more prepared for

the um interviews to come so um I I'll

kind of quickly talk about um what's

going on on the um AI product management

site at PM exercises we think that we

try to um bring everything that I talked

about together um during the cohort um

some people have been going through it

right now um so I want to talk about

this slide quickly and then I'm going to

pass it to Kai to kind of dig more into

it uh but like one thing that I'll also

mention is that um you will get access

to all the learning material for a

period of three years this is very

important I think especially given the

industry is evolving and you get access

to experts and um there are a lot of

experts right there is like um the

instructors in the learning program um

there will be um guest speaker opportun

unities that will also involve invite

people from the previous cohorts too so

you'll also be able to kind of attend

those uh and then there will be like a

good opportunity for you to kind of

learn about um some of the emerging

applications in Ai and um you know what

sort of PM roles are evolving um in each

of those areas because you want to kind

of play in in an area where not a lot of

players are out there or like um I had a

a teacher back in school uh he used to

say you want to fish in a lake where

nobody else is fishing fishing right so

that's kind of like something for you to

Define and you know what do you define

fishing maybe it's your expertise maybe

it's the fact that the industry is not

being paid attention to but hopefully

you'll get a sense of that uh you'll get

access to the community of AI product

managers um we we meet every Saturday as

you're aware um there'll be some of job

opportunities but I want to kind of

emphasize and say um you know this is

very important for you to kind of be

aware of because we're happy to do the

intros but um if you kind of you're not

attracted to the um employer and you

know you won't pass the evaluation right

you're just going to burn those chances

so uh there's no guarantees or anything

in here but um you know if one of the

cohort members right now or alumni are

interested please reach out to us and

we're happy to um do intros and help you

out but um let's make sure that your

package is attractive um and it's almost

like a no-brainer for the other side to

hire you right um and we do a lot of

kind of like team based workshops and

product building um anybody who

graduates will have we can talk about

like a particular PRD that they worked

on um and it's got a very kind of a VC

style format towards the end where you

actually get to present your idea um to

um a panel of judges and uh they'll

evaluate you and they'll give you real

feedback I'm really impressed by um the

quality of the projects that are being

um developed right now we have about um

10 projects and um they're really

amazing projects a couple of them are

totally fundable so uh very impressed

and here's the good news because you

work in a form in a team um you'll learn

a lot uh from your colleagues um a

couple of people asked that we assigned

them like uh study partners and we've

done that actually more than a couple

like about 10 people and um you know

that's one way for you kind of get more

from the community and another way is

you're going to be part of a team um

that's going to be uh developing a PRD

that you can even turn into a product

after the graduation

we'll also give you some additional

bonuses some access to the PM excises

platform you get access to um the

product the assistant for AI for product

managers on product monkey it's a tool

that converts your uh product manage uh

any design files into detail enging Tas

and uh prds and this is a really good

tool for anybody who wants to uh

basically save time and kind of

Outsource some of their uh monkey tasks

um and these are everything here is very

Hands-On as some of the people in the

current cohort can tell we do spend some

time kind of going over your resumés uh

giving you feedback and we're pretty

honest to be honest like sometimes it

might feel uh very harsh but we we

optimize for your success so we tell you

look you need to go and like really

improve your resume this way uh for

example if your resume doesn't mention

anything about AI uh there's no hint of

it uh it's very unlikely that you going

to be successful right um in learning an

aipm job so um if now you have to have a

resume that really speaks and breath um

AI product management then that means

you have to do a different set of

activities and sometimes means a couple

months of work from you to kind of reach

a point where you can proudly present

your resume for an aipm role and uh

we're happy to provide all that

guidance and um like I said uh we will

be happy to do intros um if you're ready

and um you know at some point um I've

said to people if you're very interested

I'm happy to go ahead and do it right

now but you got to make sure that you're

ready if you want to be successful at

least on your side uh so that's a little

bit about um you know the aipm learning

program that's starting um in May uh

good news is that uh we're going to have

three different tracks like one is on

Tuesday Tuesday evenings another one is

on Thursday morning and another one is

on Saturday morning so hopefully um

anybody who's kind of considering

attending and they're worried about

their time zones and whether or not

they're not they're they're able to

attend they'll probably find one of

these sessions uh to work for them and

they can attend that and um over time

they'll kind of build their team and

they'll become more familiar uh with the

rest of the community and um this is

going to be a great great uh Community

for you and for your career going

forward uh so with that said I want to

pass it um to um Kai I'll quickly talk

about this one last slide too uh but

like who are the cohort participants

really a couple people asked that

question um earlier uh these are mostly

people that are existing product

managers I would say about 90% of or so

um of our uh cohort participants are

existing product managers that worked

for um you know anywhere between two to

three or even like 105 years as product

managers sometimes they're directors or

VPS um we have a few entrepreneurs

um in the world of um AI product

management um but one thing I can tell

you is you're not going to get um the um

kind of basic um product management 101

that's not what this cohort is all about

um this is about U much kind of further

than that much deeper than that um so

thank you so much for listening to me

I'll pass it to Kai maybe Kai can talk a

little bit about um the um the program

and we can go from there yeah thanks V

so uh let's talk about a little bit more

about the the content of this of this

cohort but before that let's actually

take a look at what the what are the you

know desired or required skill sets for

aipm uh just want to make it clear all

the uh all the skill sets for the for

traditional software product managers

are still required for being a aipm this

are you know uh user empathy you know

business sense product design technical

depth but additionally to be a

successful

aipm uh you need additional skill sets

uh first of all your AI ml technical

foundations this is besides the

traditional software development uh

technical foundations now you need to

know uh what is AI what are different

types of models how to build the models

and their applications how to manage the

model life cycle all things like that

and then the ml problem definition so

after Define your defining your business

problem how do you actually translate

that into a machine learning problem and

more importantly do you even need

machine learning to solve this problem

do you just use the traditional

traditional way or do you actually need

machine learning so that's something as

a PM you need to decide so you need to

know how to do that and third is about

data do you understand data where to

collect data because the data is a key

part of any of the machine learning

systems do you do you know how to come

up with a successful data strategy

things like what are the data sources

how to collect the data what type of

data do you need to solve this exact

Machinery problem and how do you process

data and uh all the way to how do you

visualize the data at the end right and

then uh very importantly how do you

actually manage the machine Le life

cycle from uh starting from collecting

data as we just mentioned to training a

model writing a model deploying mod

model and the monitoring model and if

the model performance de rates how do

you retrain model with a fresh data and

the risk risk medication your machine

systems will make mistake that's just

the nature of machine learning when that

happens what do you do do you have do

you have a fullback plan and also uh

risk uh management also applies to the

the project management side of of ai ai

product development and because the

product development life cycle is is

very iterative it's uh more way more

complicated compared to the traditional

software development so how do you

manage that uh project efficient AI

project efficiently that's something we

we need to talk about and

explainability um usually the

traditional software is easy to explain

because it's just followed by rules and

but AI product sometimes we treat is

treated as a blackbox but a lot of times

you need to explain the your model

behaviors why your model is making this

prediction uh especially when you apply

AI in space like uh

Financial uh you know

Financial finance um and also you know

uh when you apply this to safety or

legal to uh and law so you have to

explain your model model model

performance that's something as aipm

need to understand how to do it and

lastly responsible AI how do you detect

the bias in your system how do you make

sure it's actually fair to different uh

user cohorts

um can go to the next

St so to help you develop all these uh

skill set we just mentioned uh we

purposely designed the agenda for this

cohort we will start with the a IML um

Foundation we talk about what are data

way to collect data what are features

how to do feature engineering different

types of machine learning models and

algorithms how where to apply them how

to build them and also how do you

evaluate your perform your model

performance and your application

performance and a lot more and then in

week one we start with the AI problem uh

product problem

definition and we introduce a framework

to help you decide whether your problem

uh is um is good for need to solve or

you can just use the traditional

traditional rule based system and we'll

run a live Workshop in the live session

to apply that framework to solve some of

the uh you know the scenarios and use

cases and in week two we move on once

you have the problem defined in week two

we move on to the to the design we

introduce the key principles to design

the AI products and again we'll

introduce you a design framework for AI

products they are 15 Steps I think it's

more than 15 steps you need to follow to

Define to uh to design a AI product

properly and again we run a live

Workshop uh there's some uh scenarios

you you will see how do we apply this

design framework to solve those use

cases and after Design After product

design is done after your requirements

are already now you can pass it down for

your engineering to build a product and

that's why that's what we're going to

talk about week three the M Ops how do

you actually build and launch the uh you

know build the machine plat build a

machine product and launching into

production and also maintain this exper

uh its performance in

production and in week four we finally

talk about the once the model is ready

application is built now we talk about

AI product launch and also we introduce

a framework for to for you to apply the

responsible AI to your product make sure

it actually works as as um as expected

so you can probably tell uh through week

one and week four we are actually focus

on each part some some part of your

PRD right we start from prop definition

then move on to design requirements then

move on to Builder product and then

product launch so that's exactly what

we're doing we aim to actually build a

End to endend

PRD uh through this cohorts and that's a

PRD you can actually take to engineering

team to build a real product and at the

end of the cohort we have a bonus week

this is where this is when all the teams

got to present their product ideas just

like a product pitch so yeah very

exciting content uh hopefully I'll see

all of you there back to

you all right thank you very much so um

at a very high level um anybody who is

interested in applying please use this

URL so um I've shared this on LinkedIn

and anybody who's on Zoom I'm just going

to share it with you um in a second

after I stop sharing my screen uh but uh

next cohort starts on the second week of

May um and uh please feel free to submit

your applications we're reviewing them

we've already got a lot of people who

have signed up for the next cohort so

very excited um that uh we're going to

be a part of this uh program with you

guys um so yes I'm going to share the

link in a second uh chatan I can see

your question uh your comment uh but uh

let me kind of take a second and then

see if uh people have any questions I we

would be happy to um answer them here's

the link um I'm not sure

why give me one second let just like

quickly share that I'm not sure why it's

kind of sharing it the way you're seeing

it um the ending is supposed to have ai

product manager um but uh let me see if

there are any other questions that are

posted um does anybody have any

questions anything that we need to

discuss hopefully to raise your

hand I'm going to stop sharing my screen

kind of make it easier for myself to see

what's going on oh perfect thank you Kai

um and I've shared um the um the link

the link on comments as

well um okay somebody messaged me asking

um what what does like kind of personal

support look like so um as part of the

program uh you're more than welcome to

uh book at time in my calendar or Kai's

calendar or Corey's calendar and we're

happy to kind of give you um any advice

or any help that you would need at a

personal

level um but one thing you know we kind

of emphasize on is to make sure that um

you come to us for like things that are

very important otherwise um there will

be a community to support you we'll have

a forum that you can post questions now

um we start kind of rolling it out

halfway through cohort 5 but now cord

six The Forum is going to be kind of the

default so uh that'll work great as well

um and yes um somebody's asking if I can

send them a copy of the presentation yes

I'm happy to do that as well um so we'll

share that link afterwards uh but yeah

so like you would be able to um have

access to us as well and um as you're

kind of working through your PRD

sometimes um you might come across

questions or you kind of get stuck

somewhere um we will be giving you

feedback we'll kind of guide you through

it but uh we intentionally not get

involved in like certain steps like um

giving you an idea for example for a

product right uh because um I brought up

a really good point earlier in the

school where is like their PMS they're

supposed to be able to do this

themselves so um we're happy to help you

with those things if if you kind of get

stuck but um I think the goal is for you

to kind of over time get more and more

comfortable with um asking these

questions yourself um and another person

asked if the learning material will be

available afterwards um the answer is

yes they'll be available and like I said

because the industry is like kind of

evolving so quickly um there will be a

lot of like new learning material uh as

time goes by and um all this new

learning material will be available to

you for the next three years so um

hopefully that kind of put you a these

that what you're going to go through um

is going to be relevant like a year or

two from now because you can kind of

rely back on it and kind of stay updated

with what's going on um okay one more

private question like um how many hours

really you need I think yeah we talked

about that like I would say five to

seven to get the basics and like kind of

really understand what's going on in the

cohort but if you want to get the most

out of it like like you can spend like

10 to 15 hours to be honest like we have

a lot of content that we call them

optional um you don't have to

necessarily like read them or like spend

time on them but um if you do I think

it's a good thing um and um there's a

lot of like recordings from like

previous U sessions that we've had or

like guest speakers where we had like a

CEO of a company come in and kind of

shared their experience from a product

perspective you don't really have to

listen to those things but I think what

happens is you kind of spend more time

in the space uh you get more and more

comfortable uh with the language and and

you get better at it uh so that was

another question that somebody asked let

me see if there's anything else that's

coming in okay no I think that's it uh

for now um anything else that anybody

would like to add otherwise I will be

wrapping up the

session okay well thank you so much

everybody for coming in I really

appreciate it um and uh I'm very excited

to go through this with Kai again the

next cohort I think this cord was

amazing hopefully people got a lot of

value out of it and um yeah looking

forward to um having a bigger uh

Community um of AI product managers

right um so thank you so much somebody

asked how many weeks is the cohort that

starts in May so yes like like I said it

would be um four weeks um but it's four

weeks um we have one week of like kind

of the we call the Bonus Week at the end

uh where we still present give you guys

some additional learning material that's

kind of optional you can take it don't

take it if you don't want to but there

will be a demo period That's when you

actually be presenting to the rest of

the uh cohort and um hopefully this will

be an opportunity for you to also um

learn more about uh what everybody else

is doing and um there will be also like

kind of a we call it like a week zero U

which is um the week before we actually

start um going deep into both Ai nml and

product management side of it basically

we spend one week U making sure that you

have like some foundational knowledge

right like you need to know how neural

networks work uh before we can even have

any sort of meaningful conversation uh

so if you add all this really it's like

six weeks um but um technically it's

four weeks because uh technically it's

six weeks but officially it's like four

weeks because there's a pre-read week

and then there's a bonus week that we

offer um let me see what other questions

people are still asking me sending me

questions I'm going to answer um so

somebody asked how can I connect with

you um shoot me an email ban pme right

um You guys probably have my personal

email uh just shoot me an email um and

we can go from there um if there's

anything related to the cohort um I'd be

happy to answer um let me see what else

if anybody else is posting anything else

one second

okay I think this is it for now uh thank

you so much everybody I hope you enjoyed

today's session I call any any final

words before I wrap it up no I think

you've covered

everything in the cohort 6 yeah hope to

see you guys in cohort 6 and uh have a

great great weekend take care

bye