I'm excited to bring you this conversation with Anand Natu from Meta, where he is a product manager working on responsible AI.

Before Meta. Anand was at Amazon, where he worked on brand protection using AI. During this conversation, Anand shared his thoughts on building large-scale AI systems, how to think about risk, and common mistakes AI product managers make.

Links

Anand On LinkedIn

Transcript

[00:00:00 ] Anand Natu: Big data walked so that AI could run in the sense that it was the democratized language of , using data to drive decision-making that eventually became more sophisticated forms of artificial intelligence.

[00:00:12 ] Hima: I'm, Himakara Pieris. You're listening to smart products. A show where we, recognize, celebrate, and learn from industry leaders who are solving real world problems. Using AI.

[00:00:22 ] Himakara Pieris: I'm excited to bring you this conversation with Ananta natto. Lotto. Annette is a product manager working on responsible AI at meta. Prior to meter on and was at Amazon where he worked on brand protection using AI. During this conversation on unshared, his thoughts on building large ScaleIO systems, how to think about risk and common mistakes. AI product managers make check out the show notes for links. 

[00:00:45 ] Himakara Pieris: Enjoy the show. 

[00:00:46 ]

[00:00:47 ] Himakara Pieris: Anand, welcome to the Smart Product Show. 

[00:00:50 ] Anand Natu: Thank you for having me. 

[00:00:51 ] Himakara Pieris: To start things off , could you tell us a bit about your background, what you're doing now, and your journey, , in product management in ai? 

[00:00:58 ] Anand Natu: Academically, my background's in engineering traditional engineering. So my undergrad was in chemical engineering, and then I spent the first few years of my career as a management consultant working across a variety of industries, but with some focus on the technology space.

[00:01:14 ] Anand Natu: I first got into product management. And got interested in AI kind of around the same time because I, I, number one, kind of wanted to switch into product management to be closer to building things, which is one of the things that I really missed after a few years working in consulting where I was a little bit further from that.

[00:01:32 ] Anand Natu: And then the, you know, secondarily, I was, at the time at least interested in robotics and autonomous vehicles, and obviously AI is a big driver of innovation in that space and. That was kind of the motivating factor for me to do my master's in, in robotics at Stanford, which is what I did to kind of transition from consulting into product.

[00:01:52 ] Anand Natu: And as it turned out, I never really ended up doing that much professional work in that space as my first camera role aside from an internship in, [00:02:00 ] in the mobile game space, was actually at Amazon. After finishing my Masters and at Amazon, I spent several years working in the consumer business, specifically on a team called Brand Protection, which is among other things, you know, the, the overarching mission of the team is to develop security and commerce features that power the experience for brands on Amazon.

[00:02:24 ] Anand Natu: Mostly for the third party selling business. And so, you know, Basically the, the purpose of what our team's work was, was to create a better environment on Amazon for brands to organically grow and develop their presence and connect with the type of buyers and the type of audience that they're interested in marketing to.

[00:02:39 ] Anand Natu: And, and basically compete with direct to consumer. Options and channels in the process. During that time, I worked that, that was kind of my first experience working with AI in a product management capacity. We worked on a number of different AI driven initiatives, including, you know, a big one that we'll get more into detail [00:03:00 ] on later involving figuring out how to use AI to basically identify the branding of products at massive scale massive scale, in this case being the entire Amazon catalog.

[00:03:11 ] Anand Natu: And then after Amazon, I transitioned into my current role at Meta, which I've been at for just over a year now on the responsible AI team here, which is used to be just part of the central AI org and is now part of the central social impact organization in Meta. And I specifically work on fairness within responsible ai.

[00:03:30 ] Anand Natu: So, We sort of research, develop, and ship software that's designed to ensure that all of the production AI systems used at meta work fairly and equally for different user groups on our services. And the, the scope of our team is fully horizontal, but in the last few years we've worked on just some of the more high priority business products that meta operates, like our ads, ads, products for, you know, ads, personalization, targeting some social.

[00:03:57 ] Anand Natu: Some models that power social [00:04:00 ] features on Instagram, so on and so forth. And, and we also do some stuff in more sensitive areas like content integrity and moderation and, and things like that. 

[00:04:09 ] Himakara Pieris: Let's talk about the brand protection use case. Can you tell me about what was the driver, what did the original business case look like ?

[00:04:17 ] Anand Natu: I'll start with a bit of context on kind of what I was working on and why it's important to to my org to Amazon in general. So at the time I was owning a program within my team called ASIN Stamping. So ASIN stands for Amazon Serial Identification Number. It's basically just the unique ID for a given product within the catalog.

[00:04:37 ] Anand Natu: The point of the ASIN stamping program is to basically identify. For every single product in the catalog, the brand that that product belongs to, and stamp it or just basically populate a field in the catalog with a unique identifier called the brand id. And there's a separate data store called Brand Registry that is the sort of authoritative source of all brand IDs that exist within [00:05:00 ] the Amazon store worldwide.

[00:05:02 ] Anand Natu: The reason why that's important is there are many reasons why that's important, but they fall into two big buckets. The first is catalog protections. If we know if we have an authoritative signal for what brand each product belongs to, we have a much more robust mechanism through which to vet and validate authentic branded selection on our platform.

[00:05:23 ] Anand Natu: Prevent counterfeits, prevent malicious sellers from representing themselves as brand agents, so on and so forth. This is, this becomes really important for major brands who. Frequently have their stuff counterfeited, like luxury brands the N F L, things like that. There are several high profile examples of counterfeits becoming prevalent on Amazon in the past, which is part of the reason my team was created in the first place.

[00:05:48 ] Anand Natu: So the security side is one big incentive for the business case of this program. The other is commercial benefits, which basically are related to. You know, features [00:06:00 ] ranging from ad targeting to advanced sort of content creation tooling that allow brands on Amazon to better represent and sort of.

[00:06:10 ] Anand Natu: Show off their brand within the context of the Amazon e-commerce experience. And the purpose of those features is to basically better, like I said at the beginning, better allow brands to develop their brand equity and brand image within the Amazon ecosystem and connect to the kind of audience that they want to connect to.

[00:06:29 ] Anand Natu: So that they start seeing Amazon as an actual home for brand development as opposed to what sellers historically have viewed Amazon as, which is, as you know, this, this e-commerce channel that's helpful for pushing volume and getting sales, but not necessarily a great place to like develop your brand image or find your really loyal, kind of like high retention customers.

[00:06:50 ] Himakara Pieris: The two scenarios that you talked about there, one sounds like you're addressing a pain point, counterfeiting of products and which has a lot of implications. And then the second one sounds like you're [00:07:00 ] trying to create new value by providing a platform where the vendors could create brand equity.

[00:07:06 ] Himakara Pieris: What was your process to assess the impact of these two sort of drivers at the start? 

[00:07:15 ] Anand Natu: Each of those two cases are directly sort of enabled or provided by features that basically leverage the data that the ASIN stamping program creates in the catalog. So the point of the stamping program is basically just to look at the entire catalog, which at the time was about three and a half billion products in total, give or take, and make sure that as many of them as possible have a brand id.

[00:07:40 ] Anand Natu: Stamped on them. And that globally, the, the data that lives like the brand ID data across the entire catalog maintains an accuracy bar of 99% or higher. So that was the internal quality bar reset. And the mandate of the team was to basically push towards full coverage of the catalog with this high fidelity brand identity information [00:08:00