AI and product management | Marily Nika (Meta, Google)

there is something called the shiny object trap and I'm always telling people hey don't do AI for the sake of doing AI make sure there is a problem there make sure there is a pain point that needs to be solved in a Smart Way once you have identified what that problem is and what that very very high level solution is then reach out and try to figure out how to actually implement it welcome to Lenny's podcast where I interview world-class product leaders and growth experts to learn from their hardwind experiences building and scaling today's most successful companies today my guest is marily NAA marily teaches the most popular course on Maven on AI and product management she's currently product lead at meta focusing on metaverse avatars and identity prior to meta she was at Google for over eight years working on Google Glass computer vision and machine learning around speech recognition in

our conversation we touch on what cams should be paying attention to when it comes to what's happening in AI we talk about a bunch of resources that'll help you get started in the world of AI how AI tools available today can already help you do your job better as a PM we also get relatively technical into what exactly is a model how models train all kinds of fun stuff like that enjoy this conversation with marily NAA after a short word from our wonderful sponsors this episode is brought to you by amplitude if you're setting up your analytics stack but not using amplitude what are you doing anyone can sell you analytics while amplitude unlocks the power of your product and guide you every step of the way get the right data ask the right questions get the right answers and make growth happen to get started with amplitude for free visit amplitude tocom amplitude power to your products this episode is brought to you

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test on the front end the back end email marketing and even machine learning clients check out EPO at geo.com Geto and 10x your experiment velocity marily welcome to the podcast thank you hello thank you for having me it's very much my pleasure we've interacted a little bit on Twitter we've never actually talked before just right now I've seen your course just kind of

all over the place your course on AI npm and so I just thought it'd be really fun to have you on and help us all understand what the hell is happening in AI and especially AI on product so thanks again for being here yes thank you I'm really excited I would love your help as a former full-time PM slash everyone listening that is a current pm to help us understand what is going on with AI and product Tech in general and tools in general move really fast you know if you're trying to pay attention to like what's happening it's really hard to stay up to date on where things are going and it feels especially hard in AI it feels like there's just something coming out every day and so I have a bunch of questions along these lines the first is just like what media do you pay attention to to stand on top of what's happening and what's new and what's interesting in the world of AI and machine learning as you know very

well subscribing to newsletters is something that's really really impactful and of course I subscribed to your newsletter but I am a big big big fan of the download by NP technology review or tldr and they're not necessarily AI Centric but what I'm advocating for and what I'm telling people is that in the future everything will be AI by default so even if you have something that's technology focused you will see a lot of AI starting to get sprinkled in there I want to follow open what you just said there but maybe we'll save it a little bit maybe going in a different direction first what do you think is overhyped in

the space of AI right now what do you think is underhyped and undervalued I would like to discuss strbp which is both under height and over height at the same time I was reading this article this morning where there are writers complaining and they're very very fearful and they think oh writing online is going to die everything we studing for is going to be replaced they're going to take our jobs and so on and I'm just like no no no no tring B and technology is enhancing our worth it's enhancing us it does not steal from us so that's what comes across right now and there are other things that are under hype like obviously CH is amazing I'm using it day to okay but there are other things AI can do in an amazing manner like I was reading a research article the the day that said that AI now detect lights so light detection whether it is for security reasons or at work or anything like that is now possible so I encourage

people to go to these newsletters and go through these online blogs tenrs and so on and just read what's happening it's not all about chading there is more there is more about AI but you should read about you mentioned the US chat GPT

in your work life talk about that what are you actually using it for even when I'm at work and I am trying to come up with a nice mission statement right when we pan or come up with mission statements it's just crucial part and it's where the core begins you want to get people excited you want to get people inspired there is nothing I can write that's going to be as good as what Chic deal with right so what I do is I literally goild the CH and I say rewrite this mission statement for me and it just even first try produces something which is fantastic so that number two it helps me create user segments in a fantastic way it will think of user segments that your mind wouldn't even go there like it just wouldn't go there and it will provide the motivations it will provide the pinpoints and you just come up with ideas you as you read it and then the last thing that it does is it provides ideas for you that AI en Hance

so I just use day today even proach my day to-day workflow but I'm not making it do my job for me I'm asking it after I have already had a mission in my head and what it is I want to do so is the way you're approaching it is you just put in come up with a better mission statement then and then you give it your version of the mission statement exactly interesting and you're saying that that comes up with a better mission statement than the one you had it's better because the mission statement is going to be read by all disciplines it's not just going to be read by PMS that already have a lot of context and understand it's going to be read by leadership by Junior people by stakeholders by other departments by competitors and you needed to be on orent and in the words that are meant to be understood by everyone even a kid could understand and they would get inspired by it as well and then you also said use it for

personas how do you actually frame that prompt with J GPT let's say you're working for a specific product area and you know you want to create some fitness band so you would say something like who would be interested in a fitness band that doesn't have a screen and it will provide a bulleted list of people like hey young professionals that they interested but don't have enough time people that do not want to charge their were of those every day then the list goes on it's just fantastic you were talking about how you

think the future of AI is it's the default and is what you there that it's basically baked into every product we use and it helps the user do better things it helps the product work better is that what you mean or is it something else I believe that old product managers will be AI product managers in the future and this is because we see all products needing to have a personalized experience a recommender system that is actually good I mean you cannot watch to Netflix you cannot even watch a movie without needing that after you you watch white L like stranger things you will want something similar to watch you're not going to want like a romantic thing to be suggested or recommended to you right also automation is another thing we need to keep improving in society we need to keep making technological advancements you're not going to be able to do that if you don't have an AI Centric view in every sector that you're

working on when you say that every PM will be an AI PM is your thinking that you'll be using AI Tools in your job as a PM or that you'll be building AI into everything you're building how do you think about that I think it's that you will need to get comfortable with having a partner that's a research scientist and you all need to understand that these people could produce a smart model they'll be able to do some automations some personalizations some recommendations on in a lot of people feel uncomfortable with a lot of people don't know how to approach the researchers a lot of people don't like the uncertainty that research has a lot of PMS are very very used to okay I'm going to do this I'm going to lunch I'm going to do this I'm going to lunch whereas when you're working with research it's more like we're GNA try this and then in a year if it doesn't work out we're GNA shut everything down

and people of complain so I feel that if people get more used to uncertainty and research things are going to be good convenient for them I thought you were comparing chbt as like a researcher you're working with but you're actually saying people will have PhD researchers on their teams helping them build models into their product to make their product better is that is that what you're saying correct this is exactly what I interesting and um and from a product perspective I can imagine like three bubbles in my head so you want to find the intersection of something that's desirable by users something that is going to be a viable business and something that is going to be feasible from a research scientist and Technical perspective and then when you have that it's just going to be a fantastic product for fastic launch that you can run with so yeah whenever I say researcher I mean research scientist

that can produce an AI machine learning model wow didn't think about how every

cross functional team might end up with a research scientist interesting interesting for PMS who are curious about learning how to do this stuff what are a couple things that PMS today who are have no experience with AI what can they do to start learning how to build AI tooling into their products understand what the hell is happening in the space of AI this is a good question and I guess the the message I want to pass is you shouldn't be overwhelmed by these Technologies if you don't have a technical background because you can learn these things and as a PM you will never need to actually train or code also even if you want to train there are no code approaches for training models but T question question if you're working on any product you can always sprinkle in a smarter feature so you can make it more secure you can personalize it you can enhance it with like fraud detection you can make it more ethical

if it's Healthcare you can make it faster you can make it more accurate if it's shoping you can create better recommendations basically anything where you can get data behind the behavior users can be improved with AI so I guess it's all about changing the mindset of PMS taking a step back and just thinking about okay I have all these data that's just lying and sitting around what is it that they can do with it I've been meeting PMS that said oh we don't have any we're not collecting any data we have any dashboard so even that is a huge first step towards Ai and then just start thinking about it what you could do just hire and get a data science intern and just see what they they are going to do there's there's just so much people can do so say you want to start investing in some sort of model some sort of AI within your team you're saying maybe hire data scientists who can help you start to build something that you can

start integrating is that your advice on the first step of once you start you want to start getting serious about building some sort of AI component there is something called the shiny object trap and I'm always telling people hey don't do AI for the sake of doing AI make sure there's a problem there make make sure there is a paino that needs to be solved in a Smart Way once you have identified what that problem is and what that very very high level solution is then reach out and try to figure out how to actually implement it and there's a definition I like hearing I usually say that generally PM helps their team and their company build and ship the right product but the aipm helps their team company solve the right problem so if you want to get into aipm figure out what the problem is that you will get the data scien to create a m for solving but there needs to be a problem there needs to be audience there needs to be a

user and a pinco for it what are signs

that AI may not be a good approach to solving a problem you're said that you know and this happened on a lot of my teams oh we're going to build a really cool model it's going to do something really smart in this case and it often ended up being very low Roi investment and took like six months to a year before you even knew what the hell was was happening do you have any thoughts on signs that maybe this isn't a place you should be putting a lot of time into AI versus like this is definitely an opportunity yes we should invest a lot of time into this don't do it for your MVB it makes zero sense do not waste time of data scientists that can train models with us using powerful machines that are going to take weeks to train this is because if you you have an MVP and you just want to get buy in for an IDE or feature that may use AI in the future fake it create a little figma product type and just show it some users

and just F what the AI is going to be doing so I a lot of young early stage entrepreneurs re talk to me they say oh how we should we train this model to do this and that because we want to prove that there is a market no do not use AI you should use AI where you think you already have some data or data from an adjacent product that you feel you can leverage for your own product to create something that's meaningful the accommodation oration we talking about but not for an MVP please people this is this is my advice how much data do you

think you need for AI ml to have a chance to contribute you have like a heris of if you have anything less than this it's not going to work at all this is a good question and it honestly depends on what you're trying to do if you're trying to classify if the photo is a cat or a dog obviously even if you have I don't know like 15 20 labeled photo that's going to work but if you want to create Voice recognizers or complicated NLP applications you're going to need thousands thousand of data and and this is what's making this m be isy right AI systems are not easy to devop there is a life cycle of a machine learning um project and after scoping you need to figure out oh God how much data do I need where do I find this data as well right how much data sometimes I've seen people synthesizing their own fake data just so that they can have something to train with and test their models but the exact amount is hard to

be Quantified especially from PM like I'm sure data scientists have a different opinion yeah my guess is most startups are going to have nowhere near enough data to build their old model and make get something really interesting so

do you have a thought on when it makes sense to try to build your own model try to train your own gbt type thing uh versus use something that's already out there like say gbt or mid Journey or all those guys if you are a big pck company and you're offering a service that is going to do spe oration or that it's going to have like their own trity you want to use more data and more diverse data to train and retain train because if you don't then your quality is going to be the same as every other companies there are agencies that are selling data packages of data that are ready so you can get them and train your models but the question is if everyone takes that exact data set then the quality that every single cond is producing is going to be the exact same so you do want to diversify you do want to collect your own data and I guess a good question from a p perspective is when is the quality of

your product good enough to launch and that is like a really interesting point because it's totally your responsibility as a p to decide okay the recognition of whether this phold is a cat or a doob is good enough for the users it's like 70% accurate 80% iade where is the bar where do we launch and that's why I'm like the aipm r is so cool because you have problems like that to solve that no one else has kind of tackled before so it's all on you we've thrown out these words

model and we talk about training models do you have a good succinct kind of explanation for what a model is for folks that haven't you know that aren't that Technical and then just the general idea of training a model like what is a simple way to think of here is what a model is so I have a three-year-old girl and I'm teaching here about life and everything so I was recently teaching here about the animals and you know you explain things to her once or twice like what the MTH is or Rhino and so on but you will end up training your kids's brain by repeating the same information again so you will say hey here's what the Rhino looks like here's what the neone looks like here's what the orino looks like here's what the neone looks like and once you've done this enough times then your kid will see an animal in the street and they'll be able to rub them mag and say oh yeah that's like the r what we're talking about this is

exactly what a model is the model is like a kid's brain it has the ability to take an input which means it has the ability to take an image and say oh I recognize what this says that looks like it Rhino but I'm 70% sure about this so it will output the probability as well of the certainty and you said image but it could be text for say jat GPT in the future I imagine video there's also voice like whisper that's an awesome explanation basically it's try to recreate the human brain is uh is a nice way of thinking about it and then training a model can you talk about what that means the purose of training the model for example is providing a lot of images that are legal and say hey here's what C looks like here's what do looks like and we're talking about thousands and thousands of data data sets for this and once you do this there's a process where the model is just processing this information and it's learning it's find

patterns through it and the patterns are not in the form of oh if this is gray then this means this no it just learns in a smart way how to identify specific things that we don't even understand and then it's able to Output the the probability of whether a photo is going to contain the C do just conceptually what is the output of the training is it code that is autogenerated with these decision trees and weights and things like that is it a database of weight like just conceptually what is the output of a training that becomes a model what's the simplest way to think about that so let's spee spee is a great example for example I'm talking to a device which is like a home assistant and I say hey what is the weather like today this is going to take my voice and audio and it's going to process it and the output is going to be a transcription so it's literally going to be text that corresponds to what I said

to it thinking about the stuff you've

worked on at Google at meta anywhere else you worked on side projects even what are some of the cooler applications of AI machine learning that you've worked on contributed to or even seen that that you can talk about I imagine there's a lot of sensitive stuff too going on one thing I want to talk about is the team I used to work for for Google which was the arvr team and they were working on an airglass and actually they they had a video on last year's Google IO they were able to have the Google Glass on someone that spoke one language and then this other person will s in front of him that spoke a different language and the glass would take as an input the audio that came from that other person and it would transcribe it it would translate it and show it on the screen for that person in their language so we're talking about the ability for this devices to unlock the borders of communication and that is not science

fiction this is what amazing and mind-blowing there's no science fiction anymore these things are real the technology is here it's just a matter of connecting the pieces to the puzzle in order to see them coming to life so I think that one was the most one of the most impactful things I've ever seen I remember that demo it was pretty incredible okay so thinking a little more broadly do you think chat GPT or just say GPT 4 or J GPT 5 GPT 6 you think at some point this will replace product managers something I see on

Twitter a lot of people are like oh my God product management is dead this thing made my product requirements document for me or you talked about how makes your mission statement better you think there's a place where PMs aren't necessary anymore oh absolutely not as I said like it makes everything better if anything it's going to free out time for me to to do other things that are less Ts for example I am running so many projects and they all need their p and the P these have all all these areas that are common across across all of them if I had a system that can actually write the TD stuff for me so that I can focus on the more strategic side of things that would be incredible it will make us smarter if anything it will unlock new areas of product mindman that we haven't realize that that are there

are there areas do you think with your kind of vision of all PMS will be AI PMS are there areas that you think PM should invest more skill-wise or areas they should less focus on invest because say some machine learning model is going to do that for them I'd like to see people being less overwhelmed less intimidated less afraid to start learning how to code how to train a little model on their own this is because even if you know trbd or these no code applications may be able to do this for us it gives you a different approach a different mindset a different if you want confidence to know how things work and here's a silly example I was learning how to play the piano when I was young and when my teacher came in I was like oh I want to learn how to play this cool song There were some songs that every night and she said no you need to start with a classical music and I just hated it at the time and I said why do I have to do

this because she said if you learn the fundamentals and how you know where things started and the beginning of music it's going to help you along the way to create music on your own if you want to and she went write like I I just loved it so it's the same with coding I encourage people to just take an online course understand more get your hands dirty pair up with someone else that's in the same boat as you because this is going to give you the skill set to understand how that tool that's going to help you in your day-to-day was even created in the first place instead of blindfolded just trusted to do your job this episode is brought to you by Pando the always on employee performance platform how much do you love the performance review process yeah it's timeconsuming subjective biased and there's rarely any transparency with the rapid shift to distributed work it's a struggle to create the structure and transparency

that you want to help your employees have the highest impact and growth in their careers p is disrupting the old Paradigm of Performance Management including a continuous employee Centric approach so employees stay engaged see their progression in real time and know exactly when and how they can level up with Pand managers can leverage Competency Based Frameworks to effectively coach and develop their teams and align on consistent growth standards resulting in higher quality feedback and higher performing teams visit p.com Lenny for more info and get a special discount when you sign up and reference this podcast that's p.com Lenny for someone that actually

wants to do that and learn to code which I love that advice do you have any resources places that you point people to for learning to code getting started down that path it depends on what type of learner you are there are some people that like learn offline so just go corsera there are so many courses there is an amazing one actually introduction to AI by Stanford that they we encourage people to take a look that but I know that a lot of people don't like don't have the time don't have the discipline to actually you know take time off or like after work after they put their kids to sleep to just do it so if you enjoy learning with with others if you're enjoy being part of a team if you enjoy going through a journey together then I recommend these resources so there is something called Career fundry which is a fantastic online coding school general assembly and then coding Dojo I I was actually gave talks ages

ago called Dojo about Python and all it takes is just a few weeks of your time and passion and just for you to roll up your sleeves and just realize that this is not intimidating and realize the benefits you can get by learning up awesome thanks for sharing those while include links in the show notes going back to a PM trying to become better in AI if you think about a PM that's kind

of early in their career and wants to become a very strong AI PM I know you have a whole course about this which we can talk about now or or later whatever is easier what should that PM be doing we talked a bit about learn to code maybe start playing with tools what else do you suggest PMS that want become really strong aipm do now and invest in so I do have a course that's coming up on February 6th on Maven which is for current and aspiring product managers that want to build AI products but I also have offline recording so I have the same course and then offline basis on my website I'd be happy to to talk to you here chat to me about this what I feel people should understand is what it takes to manage an AI product of course people are very familiar with the stages of product product development in general but AI product development is different as I mentioned before sometimes you're actually managing the

problem and Mar the product and you're trying to figure out if there is a problem that makes sense to be answered by a smart solution so it's kind of a a very interesting and more complicated process than regular product management so number one figure out how it differ from General product management number two if you're already at the company that is actually having AI researchers and AI research scientists I encourage people to just we talk to them and Shadow them and spend an hour of their week just just talking to them and experiencing what they're doing this is going to open your mind this is going to give you so much context as to what it is and and the endless potential that you can identify there awesome and is there anything else you

want to share from your course you think might be interesting to folks so we talked about why it's awesome to be an aipn but I do want to call out that there are few challenges that people need to be aware of number one and I kind of mentioned it before is the uncertainty you may have been working on all these incredible research and ideas in hypothesis but then when you actually train the model the results you may be getting may not be optimal may not be answering the questions or the hypothesis that you actually had in mind so that's number one you need to be able to encourage the team throughout this process because you're like the captain of the ship you needs to be the one that's kind of trating the team making sure keep going number two you are going to have to be Point not you are going to have to change the option and managing this from a leadership perspective can be tricky and it can be challenging number three we

talk about data but getting good data is hard you may need to be creative figure out ways for data collection that you never thought you to do you may get on the street and ask for people to actually contribute data for what is you're doing like you need to be able to and willing to do everything and the last thing is from a career trajectory usually product managers get ahead the more they launch but if you're in in a research or you're not going to launch as often so you need to make sure to clarify with the hiring managers early on hey what does progress mean how am I going to get assessed in the research work which is different than what I've been doing so far so it's challenging but I always encourage people to flex different muscles and this is like the zero to one muscle that I think is is just crucial when it comes to Prof one

this actually is a great segue to a question I definitely wanted to ask which is around getting buyin for investment at a company for ML so there's sometimes like all this energy for like a zero to one let's just just try something sometimes not but that maybe that's maybe there's a two-part question here do you have any advice on just getting buying for we want to try something with theml it's going to take us six months to figure out if it's worth the effort but we think there's something here and then sometimes there's like a lot of energy initially and then you get some win like your search ranking is smarter and it's great but then maintaining that having like all these really expensive people working on just tweaking this model and continuing to make it smarter and a little more efficient often it's hard to to get buying for that sort of team do you have any advice on initial kind of

buying let's try something here and then down the road just like keeping a team going trying to make this thing smarter and smarter people should know that there is an excellent source of inspiration and something kind of do risk things which is adjacent prodcts maybe the company has already launched a product that has been successful that was AI first and whenever I tried to convince leadership about something that I want to do that's kind of a big bet I always use examples and I'm like hey this seem crazy at the time here's how it work what I'm proposing is very similar to this crazy thing and then I propose a little C CL like hey if that doesn't work out here's the roll back plan here's kind of the Maximum Impact you will have done in a negative way which is not going to be too much and you kind of take it all on zero and it's interesting because the more you work on this specific company

the more trust you get and if the culture is such then failing is going to be welcome so I love companies that welcome pay because you can just go ahead and do this Ro you tell me if I'm

wrong but I feel like most investments in ml are not successes and often not great uses of time I'm curious that that changes with more tooling and more kind of public models that people can plug into without having to build their own I wonder if it becomes like oh okay look we'll put in three weeks we'll get something really useful exactly and also the other thing and I want like to add on the question you asked before about hey how do you keep updated about new nich tech we shouldn't underestimate Academia and research blogs and there's a website called archive where you can see new papers come up because this is where I mean CHD and and L used to be there for a long time like there there was a lot of information on this sort of thing but it's now recent where we see that research scientists and research ORS are kind of not as siloed as they used to be so the more companies invest on Staffing this layer between

productionizing and research academic research the more PMS are going to add there then the more you're going to see this bridge kind of creating good products they're created so sometimes you have amazing ideas by research scientists but you need the PM to take it and actually figure out ways to also monetize it right that's the other thing if you're a PM you need come up with ways to actually be able to monetize and CH gpp is now free for everyone but I don't know if you if you saw there was a there was a sign up Forum that was kind of coming around saying hey would you pay for this what would be the minimum you would pay what would the max will you pay what would you like to see if you paid so having BMS brid the tap is crucial for companies to be able to take the resarch and actually come up with me full use cases for users I think they actually started charging other day I think it's

like $40 $42 a month to start using it I think people have been talking about it on Twitter I don't know if that's live yet and then you talked about research papers when I think that I always think of Tyler Cohen he has this awesome blog marginal Revolution and he's really good at sharing insights from research papers that he's reading so that's another place for folks to check out he's just like this really smart dude he's really excited about Ai and GPT in general so he shares a lot of really interesting insights about it all seguing a little bit to your course I

have a couple questions about it one is just like can you just talk about like the broad framework of your course like how long is it what do you learn what are the workshops broadly and then I have a couple follow-up questions my course is three week long it's meant for people that are either aspiring or current PMS that want to understand how to sprinkle in AI Solutions or they want to become full-time AI PS wi one is more by introduction what the product development life cycle is for regular products and how it differs for AI pant specifically and then we talk about idea creation how on Earth do you come up with ideas and I love what Steve Job said where he he used to say well users don't know what they want until you show it to them and that's exactly the mindset I want to invite to people and say hey people don't know how on Earth to use AI people would never have imagin chance to do what it does and then we

take that and we dive deep and we talk about how on Earth do you productionize something like this what are the different partners you're working with what is the research scientists and how on Earth do you collaborate and how do you partner with them how do you convince them of what you have in mind for their precious research to be converted into a product how in Earth do you convince them to trust you and and how do you influence them and then at the end we're talking about how you actually will be able to pave your pathway PM all the way from interviewing for this Ro from what good regiments look like and doing some walk interviews because the more you practice the the better it's going to be how many workshops are there through the course n workshops nine workshops okay of the nine workshops which of them are you finding is the most exciting gamechanging for someone most interesting so throughout the duration

of all these workshops people have homework and they actually take home an exercise where they need to create and develop their own AI product end to end and they can pair up with each other by the way there was this this two students paed app and actually where with raise funding which is mindblowing to me which is really that's awesome um but to continue the most exciting part is when everyone at the very end are actually presenting their work and they're actually asking questions and getting feedback and they're just really excited and PR for what they created that's a good reminder of a lot of the learning that you do is just doing it not just kind of reading about it and following Twitter can you share any examples of stuff people built after the course someone was able to actually and I keep you not create a l model that was able to take as an input a trase that they found online and was able to tell us

what was wrong if something was wrong with that patient and it's just crazy to think that you can do that within three weeks obviously it was just by photos we were able to craw online for x-rays but the concept is there that you can build something like that you can create it and to take it a bit further they wanted to create a little recommander system and say hey we think this is what's wrong with you here at the step po obviously we're not trying to play doctors or to to pretend that you we're Medical in any way but being able to see that actually functioning is just it's very that's amazing did they already know how to code the this team that built this thing they did not but part of the course is to teach people the basics that you're going to need for PM lens and there are some nood tools as I mentioned that are going to allow you to drive and drop and train these models and inut photos in it and be able to do

it can you mention those tools again because that is really interesting I know it's just like a Peak at your course but if someone wanted to start

building something like this what what are some of these tools they could check out one of the tools I would like to recommend to people is actually autoing mail this is offered by ro cloud and essentially it allows you to train high quality custom machine learning models with minimal effort you don't need to be able to code or anything like that you need to have a lot of photos and images that you have already corrected but it's not going to do the collection for you and a great application I had to see there's actually a YouTube video about this is there was this company that actually had a lot of wi turbines and what they did is in order to maintain these they will actually have people manually have huge ladders and go take a look and see if everything was okay So eventually they just got drones and they had these drones fly on all of these machines and take photos on everything and then they downloaded all these

photos and they uploaded on autl and they were able to see which ones need in maintenance and which did not and I think they reduced time from like three weeks of work to like a few hours of knowing which need maintenance and just be able to send people there so it's this type of thing that you can do on your own by applying the sort of tools and that tool is called Auto ml yes autl amazing we'll link to that in the show notes coming back to your course and

maybe just a couple more questions can you just talk about what it takes to build a course like the course you buil like how much time did it take you how much work did it take anything there you want to share they treated creating my course like a product this is like what I did is I came up with some hypothesis as to who the audience was and as to what they were looking to get out of it and I started reaching out to people and I started saying hey first of all would you like to learn from me second of all what would you like to learn what are the specific questions that you would need answered because these are people that are working full-time with have families right in order to take a break from all that he need to provide something to them that is Meaningful and there were quite a few iterations in the beginning I was focusing the course more for software Engineers that wanted to become a

product managers but then I realized no there are a lot of PMS that want to become AI product managers so I I did a little onl shift there so what it takes is make sure you find the Right audience make sure to figure out what that audience wants make sure to have the right duration one week I find it too short two weeks it was still a bit rushed three weeks is excellent because you give the opportunity to everyone to present and to get to know each other on like an offline Discord Community which is another important part and then the last thing you need to have a personal relationship with everyone so I've messaged everyone I've seen everyone's application I met with some people as well just to make sure to answer any questions and concerns because I wanted to make sure that people were comfortable just trusting a stranger like me and paying them for to provide knowledge for their course so it was it

took quite a few iterations but I was able to get there and I'm very very happy about it and I recorded it offline as well for people has anything had to change in this course maybe that's just as a last question things are moving so fast is there anything you've had to like rethink redo since you first built it I actually added bonus sections and one bonus section was Treasure B and how it was trained this is because I started using new cord in December and on day one the question I got is what is this how did it start what is going on how did they train it so I added a theic section for and in people to it amazing

anything else that you'd like to share before we get to a very exciting l around it was someone that they recommended I actually did a course and in the beginning I it was it was not in the beginning I laugh and I said wait people would want to learn from me really and of course they did and I'm teaching so many people so what I want to tell people is don't underestimate this try creating your own courses as well people me want to learn what you take for granted for them it may be gamechanging it can be lifechanging so billing coures is an amazing thing and you know we're living in the whole cation era so the course is content so go try this I find that teaching and the least crystallizing thoughts is one of the best ways to learn it yourself I imagine you learned a lot about AI much more than even came into it with just putting it together into a course absolutely and I I got some uncomfortable questions that I have no

idea how to tackle like people on day one were like how do I assess the trade-offs between these two different moments and I had to figure out how to answer these things and how Incorporated them in my course so learning from the students learning from the course learning from explaining is just so valuable so skills that we can get well

with that we've reached our very very exciting lightning round I've got five questions for you I'm going to go through them pretty quick whatever comes to mind share we'll see how it all goes sound good sounds good two or three books that you recommend most to other people inspired it taught me it's all about how to create products people love by Marty Kagan right yes that's the one cool anything else or that's the one that's the one that comes to mind you look like a thing and I love you and I have it right here it's a great thing super super cool it's about how AI works and why it's making the world a weirder place it's actually a very fun book and there's one more which is a book a workbook I recently launched with atlan car and it's about um it's a workbook for women in Tech trying to navigate working in Tech Tech it's called Adventures of women Tech workbook so that's another thing that they want to

shamelessly plug in h that's a great choice to plug where can folks find that is that on Amazon yeah Amazon amazing what's a favorite other podcast that you like to listen to I like boses podcast I don't know if you're aware of it BOS is the CEO of Facebook he has a great podcast I have not heard it I do know of BOS I will check that out I didn't had a podcast he had some great writing over the years maybe that's why he doesn't write anymore he has this podcast what is a favorite recent movie or TV show that you've loved oh my God the White Lotus people were talking about this thing I I ended up you know just trying it out and me and my husband we just binge watch the whole thing is just so different so mindblowing get you excited about going to Hawaii again it's just it's really good have you seen the second season I've seen it and it's so much better than the first which is rare I agree awesome love that show what is a

favorite interview question you like to ask in bonus points if it's AI related i' love to ask people how would you explain a database to a threey and I know it's it's kind of an AI not very Ai and but I love asking it because people are kind of think that boxing wait what did you just ask me but it's so important to be able to explain things in a single ring and have the storytelling to convince a kid and really explain technical terms to non-technical people favorite AI based tool that you think people should check out I mean we talk about tragic BT now my head is on tragic BT this is what comes to mind um well the lens out was pretty cool too right we all uploaded our photos we're able to see what it would look like as fantastic Heroes I have to say I tried being the nail version because it was so much cooler than the king L version so that's what I recommend to people try the male version

that's fun and there's actually a they actually have pets now that's what got me to download it and pay for it you can take pictures of your pets and they look so fun that's like a killer feature right there good job lensa and the app is lenza right yeah L amazing marily thank you so much for spending time with me sharing your wisdom two final questions where can folks find you online if they want to learn more and reach out and how can listeners be useful to you thank you so much uh people can find me on Instagram I also have a product Channel on YouTube that you can check out I just started it I'm getting used to the whole process I'm also kicking off a newsletter just any social reach out and you'll see all my links how do they find the YouTube channel how do they find the newsletter they been marily Nik marily thank you again for being here thank you so much ly it was a flavor thank you so much for listening

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