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Amazon bill format pdf download for Product Management

hi everybody my name is shweta ranganath I'm a senior product manager at Amazon Alexa and I'm here to talk about product management and AI so uh what is product management in EI and let's get down to basics of product management so traditionally uh product management typically means uh you understand a customer's problem and you define the business impact Associated the with the problem that could help your organization deliver value to the customer but also making sure your organization is profitable and then you uh come up with a product requirements plan uh user stories work with your software development teams and user experience teams in turn need to build um the best infrastructure and the user experience to deliver the product to the customer uh the major difference between a traditional product management uh framework versus a framework in product management AI is you would be working with a lot of data scientists and research scientists to build models and integrate the models into your product to deliver whatever value that you're trying to bring bring to the customer so some of the fundamental differences is the way you can do this is uh by actually the functions and team structure which I just talked about at a high level so in a traditional product management you would be the product manager but you're the CEO of the product and then you would have a software development team you would have a user experience team you would have some you know there you if you're lucky you have uh bies and data Engineers working with you to build analytics and tools uh to kind of build feedback systems and then the difference in product management AI is you would have a team of data scientists or research scientists that are working on building models uh to kind of you know make predictions or whatever the outcome that you want to bring out now a lot of times when you think about product management in AI uh it could be either internal or external products so a lot of external products usually are driven by a p l so you're always kind of bound by bringing business to the customer in some sort of sense um whereas product management in AI you could be building products internally uh with AI and uh you know deliver a lot of uh value to your organization and by building optimizations by building search engines by building prediction mechanisms um and I I'll get to that in the next slide where I talk about some of the examples of internal products versus external products and kind of circling back to the point that I previously made um for products in AI you may or may not have a p l so if you're building like an internal product a supply chain product you're actually still working in benefit of the organization by reducing the bottom line or maybe reducing cost for the company but you may not be actually bringing new business to the company whereas if you're probably building a tool in in a social network where you're trying to increase users uh by using data science and machine learning then you're actually building uh you know actually bringing business to the customers so this way a product may or may not have a p l when AI is included so some of the examples that we can talk about in our everyday life of products in AI that you may have already used but you don't realize that there is so much of uh you know data and uh data science and the research scientists working behind the scenes on this in addition to the product managers so one of the things that comes to my mind immediately since I work at Alexa is smart assistance so um the kind of technologies that you would use in all these different kind of AI products would be slightly different but they may be overlapping when you think about smart assistance you you need to understand speech recognition and then you need to understand natural language understanding uh when you think about self-driving cars these days uh you have a lot of data from the sensors that that's fed you all time into some sort of a model and then predicts you know maybe Collision mechanisms or give you gives you alarms based on that so this is some another example of how AI is used uh you have Smart vacuum cleaners these days so um there are vacuum cleaners that help you train uh based on your existing data so you have again a lot of sensors built into the vacuum cleaners where you let it run and learn from the the data that it has uh and then it kind of uses the data to kind of you know train itself on how to clean going further um social media feeds is another example so you go and search something on social media today and you have like a ton of recommendations and uh you know suggestions of your and your feed based on your historical searches so the way the users the way it's done is you kind of use the existing data uh based on your search and then you build models to kind of predict what you would actually like to see in future and pretty much any e-commerce platform today uses recommendation system so they look at your previous history see what you like and then make your life easier about kind of recommending on what products you would like and I think personalization is very similar to to that um it could be things like your music choices or your video choices that you would like to see based on what you've previously seen and use that data now coming to the internal tools that I actually talked about is automations in supply chain or forecasting and pricing these are some of the internal AI products that you could build to actually improve the productivity of your company or your organization now what are some of the key things that's important for actually fundamental things that's very important while designing uh products in AI um the three things that come to my mind immediately is data and data acquisition for training models so when you're building a model it's really really essential to make sure you have the right C data to kind of cover enough of the almost to say to replicate the users that you're building for and another thing that's key for AI is kind of getting the data itself so um data acquisition itself is a huge challenge by itself in EI in general so when you kind of uh go into this building AI products I would say that the key thing for you is getting the data for training your models and the second thing is um you know the means of getting the data I would say the second thing that I can think of immediately is privacy and security now a lot of these uh products that you're building is is based on um customers private data so you want to make sure that uh buying or designing your systems your AI products and also when you're acquiring the data you are giving the utmost importance to prevent any kind of um you know data leaks or data Explorations so you want to make sure you're protecting um the customers data by by choice and you do that by designing your products all the way from building the right infrastructure and also getting the data from the customers you're also building the products and making sure you're experiencing is the experience is not actually revealing any kind of private data to the customers so you want to make sure your end to end your creator to delivery uh you know create to agree with the product is completely privacy proven secure security proof the third most important thing in fact I would say this is probably the most important thing while building an AI system or an AI product is the bias and inclusivity so because so much of AI happens behind the scenes um you want to make sure that your data your AI your model itself is not biased in any way and the best way to do this is to make sure your data you see data which goes back to the first point is actually kind of uh covering all the different kind of variations that you would see in a real-time data that you would build a product for and of course inclusivity so you want to make sure you're in addition to making sure data and your models don't have bias you want to make sure the way you've designed your feature does not actually uh you know exclude any kind of uh group from a user group from actually um by using the product so you want to make sure you are building all of these bias you're making sure your your you're very conscious about your choice uh making sure there's no bias and making sure um there is a lot of inclusivity both when acquiring the data for building the models and also while designing the features itself now uh one thing that's commonly asked I would say is how do you kind of get there how do you become a product manager in EI so I can talk a little bit about my own journey in terms of how I got here and then I can talk about ways that that will actually help you get there so um I have a background in engineering so it actually uh helped me a lot to kind of already have that background and uh once I finished my engineering I did my masters in electronics in communication where I did a course in artificial intelligence that kind of gave me a foundation but although uh that helped me it didn't really understand it helped me understand how do I Implement uh AI into your world right so it took me a couple of years to do an engineering role um where I worked a lot in statistics with engineers and then helped me understand how to use data to do predictions and mechanisms and um once I finish that I think I was so so involved in my engineering role and I had to kind of pull back and understand where engineering connects to business and how you could use the data that you have to actually deliver business value and understand customer problems so it's really kind of having that bridge of um understanding where you deliver business value who your users are what problems you're solving for the users and you're not over engineering your product to actually deliver you know deliver value to the user so you do you want to make sure that you're using sophisticated mechanisms and systems to actually uh build products using AI to build products but you shouldn't be over engineering or use so much data that's not required um you could deliver value to the customer by uh doing something simple but by actually delivering we're building a model with a regression uh like a regression model which is really lightweight does not require a lot of training data I mean and that does not require a lot of uh you know uh GPU time to actually deliver your data deliver results in real time um whereas you could build really sophisticated systems that takes data from the all-time processes it and then puts the puts gives you output back to kind of deliver to the customer now uh just kind of going back to my story on how I got there um I did a course which helped me kind of build more uh knowledge in marketing and finance because I think it's really essential when you're trying trying to build a product uh whether it be it AI or not AI it's really essential to understand how your um you know how do you estimate um the value that you bring to the organization as well as the value you bring to the customer so I would definitely say if you are actually thinking about becoming a product manager it's really important to understand how pnl works um the other thing that I would I that helped me a lot is um marketing itself so I feel that a lot of vision setting in a product because the product that when you think about a product it's not just hey you build a feature and you move on you think you were all the way from um from your customers point of view on how you can set a vision and kind of develop a multi-year roadmap uh which has Milestones so you think about how you build a basic product you're you know um MVP and then you kind of add feature enhancements and experiences to kind of improve the experience for their customers so you have to think about how you do your vision setting so I feel like a lot of marketing knowledge helps you just build the vision uh in that sense and uh it also helps you understand how do you go to market with a product so if you're building a product that has a p l and you're expected to deliver uh you know expected to have incremental revenue on those kind of goals and your product then I think it's really essential you understand how you go to market through your pricing uh your placement of the products your go to market plans all of these things are really essential and for me itself I think taking a courses in in marketing helped me a lot and of course you know I did a course in data science which helped me even more I think um one of the one of the key things is um how do you understand what is required to actually build a model and I think people um assume that it's easy to understand but there's so much of basic understanding so you need your seed data to actually train your model you need to make sure you're building you're actually picking the right kind of training method so you have like um you have different kind of training methods like you use like decision trees you can use regression mechanisms um you know you can use scale and models so the different type of models that actually help you uh deliver different kind of uh results so if you are building a recommendation in an engine it's a different kind of model you would choose or if you're trying to build a personalization uh engine it's a different kind of model that you would use if you're just trying to predict house prices in your neighborhood again it's a different kind of model you you would use um and also one one major thing that I've learned over the years working uh in the AI world is a lot of um you know companies that are at the Forefront of AI use ensembles which means that you use a bunch of different techniques to build a model uh and then you basically merge them into one single model and then you use that model to deliver your results so you have like a lot of um sophistication there but it also means a lot of GPU time when you run models that are heavy so you have to think about the cost that goes into building a model while you're building the product that delivers a value to the customer and of course I think one thing that I did was I actually did a stretch project early on in my career when I was in engineering role in product management where I actually defined a product policy for customer engineering Solutions so having that extra actual real work experience before moving on to a role full-time really helps you in addition to actually upskilling yourself with uh you know with Academia learning and having a mentor helps a lot so uh for me I had a bunch of different mentors and I feel like the the Mentor that you choose is really important because I think there are mentors who help you kind of navigate your career but you need help you need mentors who actually give you um effective feedback so picking a mentor who you work with regularly who is and maybe a senior role in a product management 40 will really help you kind of getting the right feedback on building the right skills that you're missing right now uh to get to a product management role and um I feel like when you're done with all of this and when you're kind of ready to you know move on to a product management role in AI uh you should definitely do a lot of mock interviews to understand uh where you stand and what's missing in uh where you know in your current profile so a lot of times you've actually done work that that can be placed as product work but I feel like it's really understand what product management means and how you fit into giving the right kind of examples to give the interviews to make sure you actually you know uh kneel the put the nail there and get the job um so this this kind of summarizes I think the talk about product management in AI uh I'm happy to take questions outside uh obviously um uh you know of uh this time here and um my bio should be linked on LinkedIn and I'm happy to like walk through walk you through uh any kind of questions that you have or disconnect for a social coffee chat if you'd like um so I hope this was helpful for budding product management managers who are looking to get into the AI technology in general um and just helping learn the basics of product management in India of course I think the deeper you go and uh think about more about how you build products product management products and AI there's a lot that you can talk about all the ways starting from the kind of services that you choose to build AI products and how you in integrate that how do you kind of get the data to build your seed model how you actually work with scientists research scientists and data scientists to deliver value so I feel like one of the things that's more essential is while you're actually writing a writing a business uh you know um business you're defining the business impact and thinking about how you have a vision for a product it's really important to work very closely with data scientists and research scientists because I think feasibility becomes very important in executing a product in AI so I feel like all of these things are so tight-knit and if anybody is really interested in learning about this more in detail I'm I'm super happy to you know connect offline um you have my contact details just below here you'd also have my profile a link to this webinar where you can reach out to me on LinkedIn or a message if you'd like um and then again I I really enjoy talking about product management in Ai and product management in general I really hope this is helpful to people um you know who are looking to get looking to get into like a cardio in product management in EI and thank you very much for staying on and listening in on this and have a good rest of the day um thank you very much bye [Music]