Kumo’s Hema Raghavan: Turning Graph AI into ROI

[00:00] If you have your data laid out as relational tables, a [00:04] Kumo just sucks it in. So... [00:06] You just specify through connectors, tell Kuma what your schema is, and then you can just start writing predictive queries. [00:16] So the graph is abstracted away. But if you have someone like a data scientist who loves tweaking the neural network parameters. In case Constantine is the same. I would be interested. Exactly. Guilty. Exactly. You can look under the hood. And the analogy we always use is we'll give you the self-driving car. But if you want to look under the hood or if you want to drive stick, we'll let you drive stick. [00:43] *music* [01:00] - We have a brilliant guest today on Trading Data. [01:03] Welcome, Hema Raghavan. [01:05] co-founder and head of engineering at Kumo AI. [01:10] HEMO brings decades of experience leading AI initiatives at LinkedIn. [01:15] She came up with the "people you may know" technology. [01:18] and other core features that leverage the power of graph learning. [01:22] Her journey in AI predates many of the technologies we all take for granted today. [01:27] She was working on NLP before BERT was even a thing.

[01:31] With Kumo, Hama and her team are revolutionizing how companies harness AI [01:35] by making advanced graph neural networks. [01:38] These neural networks let you do AutoML, automated machine learning on any platform, from Snowflake to Databricks. [01:46] Kumo's innovative approach allows companies to leverage their existing data warehouses [01:50] in order to build sophisticated AI models faster, cheaper, easier. [01:56] You don't require the deep expertise in draft learning. [01:59] We're maintaining complex features. [02:01] You can just go straight to business value. Welcome, Hema, to Training Data. [02:07] Today we have the amazing Hema Raghavan. You are building Kumo AI, which is AutoML on the data warehouse using advanced neural networks and graph neural networks. AutoML was incredibly promising a few years ago. It was a major trend in the last wave of AI five, six years ago. It went through a little bit of a trough of disillusionment. [02:31] seeded from the forefront and companies started to store their features and feature databases and the like. Why are you focusing on AutoML? What's different about Kumo? [02:43] Okay. [02:44] So there's AutoML. [02:45] And then there's AutoML on GPUs. And I think that's the big difference for Kumo.ai. And let me give you a little bit of an example from my own career. [02:55] So I started in NLP, and when we would build systems back in the early 2000s to answer a question like, when did Marco Polo land in Asia? We would be encoding features like Marco Polo is the subject of the sentence, and it's going to be the subject of the answer and all of that. So we had to know a lot about language, about linguistic structure and so on. And then the GPU revolution came.

[03:24] that enable neural networks to come at the forefront of this technology. We don't write features like that anymore. Those intermediate layers in a neural network really learn the parts of speech, the named entities, all of those properties of language. [03:41] It's the same in other classes of problems. So in the class, the AutoML that was happening maybe a decade ago, we were looking at CPU based models. So think of logistic regression, think of XGBoost, SVMs and so on. And... [04:00] All AutoML did then was parallelize what a data scientist would have done, which was a lot of hand-computed features. And that required you to be, you had to write code to think like a data scientist. So you were. [04:16] trying to get [04:18] the machines to think like humans. Whereas here... [04:23] What we're doing as we've [04:25] We use graph neural networks with neural network technology. And you can think of a GNN as a superset of CNN, which is used for images or a sequence model, which is used for languages. GNNs, you know, allow for arbitrary structure. [04:41] And the GNNs are learning all of the features that you would normally use for prediction, predictive problems. So Kumo sits in the space of predictive AI and we're really bringing... [04:53] Transformer technology to predictive AI problems.

[04:57] Can you say you mentioned graph neural networks and you gave a great explanation? Can you explain to me like I'm five years old? Because that might be where my level of understanding is. Are graph neural networks good for any class of problem? Is it good for, you know, you came from LinkedIn where you were working on, you know, the social graph of LinkedIn? Is it good for specific types of domains? [05:18] That's a great question, Sonia. So. [05:20] Let's say you're going to put this podcast episode out and, you know, it's going to be on some video streaming site. And we want, you know, to recommend the relevant podcasts for users of that video streaming site. [05:36] YouTube. [05:38] To be explicit. If someone's watching this on YouTube, you want to recommend someone to watch it or not. Exactly. So user logs in and you not only have the content of this podcast episode, but you also have what you might have watched in the past. So you can think of that. The records of what you watched in the past are sitting in a views table. Collaborative filtering era. [06:04] Exactly, exactly. But the difference with collaborative filtering is it's just looking at views. [06:11] How can we take [06:13] you know, the view data. So the view data is a network. So coming to Sonia's question, right? There's a podcast episode. There's all the users who are watching it. So you got it in terms of, you know, you have a bidirectional graph, the users and the podcasts, but then you have the organization, you have Sequoia Capital, you have the channels from Sequoia Capital, you have other

[06:43] thinking about links across these, you know, nodes of a graph. [06:48] And effectively, what a graph neural network is learning is, let's look at [06:55] what Sonia watched in the past. It seems like she... [06:59] really likes AI. AI and baby shark videos. Okay, so AI and baby shark. And then the neural network also learns that Constantine likes AI and what would it be for you? Probably AI. Just AI and AI. Yeah. [07:19] Like history. [07:20] That's great. So there's AI and history, right? So the neural network can learn that there's an overlap between both a few on the AI pieces of content. You both engage a lot with Sequoia content and it's learning across this network, right? But the next time Sonia watches a baby shark video, we don't want to be recommending that to Konstantin, right? [07:50] with and learn across all of these edges. Think of clicks, views, all your behavioral signal that you engage with entities in this world as a graph. [08:03] And how do we learn across that graph? [08:06] Yeah. So you don't have to be a social network to have a graph. Everyone, everyone, everyone, [08:12] Almost every enterprise I know has a graph.

[08:15] FinTech has graphs because they have customers, they have transactions, they have related data. Think of a [08:23] you know, one of your delivery services. They have the inventory, the suppliers, the means of transportation. So they're all sitting as tables. They're all sitting as entities and they're all linked across each other. Graph learning lets you learn across that. That's a pretty key insight, the tables. Before we go there, that was a very smart five-year-old. I think that you have a five-year-old. [08:53] Okay, well, they're very, very smart if they understood that explanation. Like what would be, to Sonia's point, if you were five and you were going to say graph learning versus any other type of machine learning, what's the difference? [09:05] Graph learning versus machine learning. [09:08] Uh, [09:10] Easy. [09:12] Fast. [09:14] I think those would be the two things. Just, you know, low code. [09:18] I think that would be the key about Kumon. It learns all the weights. It learns all the features and discovers them over time. Is that fair to say? Exactly. And my eight-year-old doesn't know machine learning. But if you were going to write a classifier the old school way, you'd be writing features that say, okay, users in this platform, we need to look at click-through rate data for the last three months.

[09:48] months and eight months for every single video. And we discover that Sonia has a preference for data that's for videos that are evergreen. So six month windows really matter for Sonia. So imagine all of that code being written as features. Graph neural networks eliminate all of that code. [10:07] Do you think that means feature engineering goes away as a discipline or what happens to it? I think feature engineering goes away. And that's not a bad thing as such, because prior to Kumo, I was at LinkedIn for almost seven, close to eight years. And. [10:28] Data scientists love finding opportunities for the business to make value, right? And it doesn't mean that feature engineering is the place where you spend, you know, that's the time well spent. You would much rather try out N different models on N different parts of the app or whatever your business is and drive value. [10:58] if your application is where a data scientist needs to spend time. [11:02] We started this episode, you said, you know, AutoML on GPUs is different from AutoML. Yes. And so what about GPUs specifically makes what you're describing possible? Like, was it even possible to do this on a CPU? Or is it faster now? Or what's different that you're doing on GPUs? Yeah, that's a great question. So it's definitely possible. It's much slower, right? So it's very similar to what neural networks brought to the text and image spaces.

[11:32] in that we can scale these models to, you know, large amounts of data. And while these models existed before the GPU revolution, we can actually take an entire enterprise, it's like FinTech, [11:46] data and learn graph neural networks for them. [11:49] Yeah, in the previous era of... [11:51] AutoML, so much of the juice in the performance came out of ensembles. So you do these logistic regressions or you do these SVMs or what have you, and then you'd ensemble them together. Yeah. Frankly, in the Kaggle era, which was how I first met your co-founder, Yuri, in the data science era of Kaggle and the like, always the ensembles won. Even in the Netflix prize back in the day, it was the ensembles that won. And there was something to the fact that [12:16] These ensembles are just tons of little algorithms chained together. And what is a neural network, but tons of little algorithms chained together. I mean, you could consider it billions of sigmoids or billions of logistic regressions. And... [12:28] Really? [12:30] The way I see graph neural networks is you're able to discover the features and the ensemble that you chain together to actually optimize towards the solution. So to me, graphs are the most general data type. [12:42] Yeah. [12:43] And a graph neural network is the most general. You kind of you mentioned it's a generalization where even a transformer is a subset of this generalization. Yeah. The most general type of algorithm that can do some learning. [12:55] Yeah, absolutely. And [12:58] As you mentioned ensembles, something that struck me was try maintaining that in production. You have N different feature generation pipelines and an ensemble. And I've seen a world where you'd have one front end engineer change how we were logging the view data. Yeah. Yeah.

[13:19] And everything either needed to change or something, you know, one pipeline breaks and it's all done. And it's a mess to debug. So graphs give you a simple, elegant framework to debug. [13:37] get at the same outcome. It also reminds me a lot more of our brain. Yes. Right. Our brain, [13:43] we think operates like a graph and is forming and pruning connections more like a graph, even more so than a more structured neural network. And so have you ever... [13:55] Have you guys experimented or thought about that as an analogy in any way? [14:00] ideas of the pros and cons of that analogy. [14:03] I think it's very similar to... [14:08] The way I think about it is let's go back to that video watching example. Right. And if I think of Sonia as a node in in a graph and what these neural network algorithms are really good at is learning these embedding representations. Right. And on this big graph, which has Sonia with her preference for baby shark and AI or her household's preference. Exactly. [14:38] That checks out. [14:40] Your embedding vector would be pretty close to both AI. So you're close to Constantine, but you're also close in Euclidean space or, you know, in some big N-dimensional space to all the baby shark loving folks, right? And we're basically learning these representations. So

[15:03] people or all the entities in the graph, like even Sequoia Capital in that case becomes a representation. So in that sense, the idea is very similar, but what GNNs do is allow for arbitrary structure. And that's where I think it's a lot closer to the. [15:24] to the human brain, but I, because I don't think the human brain is wired as a linear sequence or as a grid as an images. Yeah. [15:32] Could you say a word about how it works under the hood? Like, how are you able to, let's say you go and work with, I don't know. [15:39] Food delivery service. How does it actually work for you to go and kind of, you know, automatically learn this graph representation and how are you training models on that? Or pick YouTube, given people might be watching it there and we're talking about [15:53] AI baby shark in history already. Exactly. [15:55] So, um... [15:57] So there's two pieces to KOMO. Historically, graph learning has been restricted to [16:08] I want to say PhDs in graph learning because it's not easy to view the world as a graph. People think in terms of relational data. [16:19] That's the most common data layout in companies. And that's largely because of... [16:26] the analytics revolution that preceded the AI revolution. So everyone thinks in terms of relational data, but really relational data and graphs have a one-to-one mapping because you have data laid out in tables. Usually an entity is a primary key in a table, and then you have all these relationships, primary key, foreign key relationships, which encode the edges in a graph.

[16:56] Automatic construction from a table layout to a graph layout is one of the innovations inside Coke. [17:04] The other bit is we've invented a language called predictive query language. [17:11] And the language allows you to specify any machine learning problem in a few lines of [17:20] that looks very much like SQL. So think of SQL with the predict clause. So we've created this very simple abstraction layer on top of relational data warehouses. [17:31] There's already a universe of people who are writing SQL queries, and we've created a language that [17:40] appeals or, you know, is one that resonates with them in some sense. So that's one of the innovations of Como. The other one is running these graph neural networks. So once you go from relational to graph, just running graph neural networks at scale. [17:56] And that, again, is something that has not been easy to do. There are a few companies in the world that can do it, and it usually takes a huge infrastructure team to build that out. Because graphs inherently, unlike databases, where you can think of some logical partitioning, graphs, it's all entangled in. So how do you split it across different machines with limited memory?

[18:26] and so on. So that's, so all of these bits, [18:30] Coming together makes Kumo easy to use. But that's it. So when we go to a company like, you know, a YouTube-like company, we'll often talk to a data science team that is looking to get faster return on investment in AI. Yep. [18:48] And but then Kumo becomes really easy to do, because if you have your data laid out as relational tables, a. [18:58] Kumo just sucks it in. So we just you just specify, you know, through connectors, tell Kumo what your schema is, and then you can just start writing predictive queries. So the graph is abstracted away. But if you have someone like a data scientist who loves tweaking the neural network parameters. In case Constantine. Yes. I would be guilty. Exactly. You can look under the hood. [19:28] use is we'll give you the self-driving car, but [19:32] If you want to look under the hood or if you want a drive stick, [19:35] We'll let you drive stick. So concretely in the YouTube example, historically, if I was in analytics at YouTube and I'm watching this video, I can look and say, hey, query all AI. There would be some. [19:46] some tagging or some system to understand all AI historically. Let's see what the trends are over time. That's querying the past. Yes. What you're saying is once you have this in this database, in the structure, you're able to predict how many people are going to watch AI videos in the next several weeks. How many are going to watch Baby Shark videos? How much are they going to spend? What is going to be their monetization? What are their ads? What else can you do with this? So you can say, is this user going to churn, for example? Yep. Right. And then you can say,

[20:16] most. [20:17] relevant, [20:19] video that I want to show this user in order to retain them on my platform. Right. So I want to drive value for my business. [20:26] Given the past videos that they've watched, what's the next video to watch and so on. And we can also do demand forecasting. So we have customers in, in fact, we have in the healthcare sector and they use Kumo to forecast demand so that they're well stocked on their emergency room. Mm hmm. [20:51] So the applications of using Kumo go from consumer to healthcare to fintech, where fintech we see applications in fraud, for example. [21:07] Just is this user's behavior suspicious? Should we flag the user? So on and so forth. So think of any question which says, [21:17] How much? I love the use of query the future. How much? [21:23] Is this event going to happen? [21:27] is what's the next best action? [21:32] For this user from an action space? Those are all the kinds of questions that Kumo can help answer. And I love that you said analyst because Kumo aims to be as AutoML as you want it to be. But we also have a Python interface. So you want to be...

[21:54] A neural network expert, you can go all in. [21:58] Cool. [21:59] It's the brain. [22:00] It's the brain. It's the analytical brain. Out of the applications you've discussed just now, I would imagine, you know, there's such classical ML problems that you discuss. Each of them probably has a five person fraud team and a 15 person demand forecasting team. What do those ML people think when, you know, when Kumo is pitching the company? Like, walk me through that spiritual journey. [22:30] I'm okay with it as long as they don't have VC prediction. [22:35] So for a lot of the companies we work with, the data scientist is excited about Google. As I mentioned, writing those feature engineering pipelines comes with maintenance jobs to maintain those pipelines. And that's not where they want to spend their time. [23:00] direct business impact. So did I push that ad CTR model out this quarter? Did it drive X percent revenue? So a lot of our customers will come to us and say, you know what? I signed up for X percent revenue, but I'm only one third of the way there. Can you guys, you know, help us

[23:30] And within four weeks, we'll... [23:32] Almost always, I'm trying to think of a case when we've not shown value, but, and I can't remember one, but we've, uh, [23:40] We've always shown value within those four weeks. Wow. Yeah. So you convert them into believers. Into believers, yeah. And it's about where you want to spend your time, right? So I think once they get hands-on product, many times people will come in and say, oh, but feature engineering is where I spend all my time, right? And how can you say that I don't have to do it manually anymore? [24:10] journey. And then we'll also remind that once they get hands on keyboard with the product and they realize that the journey in Kumo, it's not completely automated away. Right. Because we say a data scientist knows their business well. [24:25] So if you're going to define churn prediction for your business, [24:30] maybe on YouTube, [24:33] activity around in the last 30 days is a good predictor of churn. So, you know, you want to bring your events table with, [24:41] a 30-day window. The schema, the actual structure that you expose it to. Or the window, right? So because these are all queries and these are all parameters in the queries. Or you could play with 90-day or 365-day activities. So these are all queries. You can [24:58] On my system, the best predictor of churn is behavior in a 365-day window, and I didn't even know that because I'm spending all my time looking somewhere else.

[25:09] So the data scientist spends a lot more time [25:13] finding the relevant, [25:15] tables in their organization that are going to, you know, bring value and then finding those, the right query formulation or the right business formulation. In this case, you know, for example, churn, what's the right definition of churn for my business? And once they see that, actually, they really, they realize that this is a lot more fun than what I was doing before. Totally. Yeah. You mentioned tables, structured data, schema. That naturally leads me to think [25:45] Snowflake and Databricks, a lot of companies have spent the last five years heavily investing in their data warehouses. How do you work with the data warehouses? Okay, that's a great question. [25:57] So at the outset, we started as a purely SaaS company, you know, emulating a lot of, you know, the principles from the Snowflake architecture, looking at their success stories. [26:12] One thing we realized is that data scientists, though, they need to see value on their own problem because they're so KPI or business impact focused, showing them value on data. [26:26] uh, [26:27] A cattle data set doesn't really count. So the easiest way to show value is, of course, when they can connect to their own data. But connecting to your own data on a SaaS product means you go through a huge security review through the company, which can in many organizations can take a couple of months. So that is.

[26:53] We wanted to reduce that friction. And we started partnering with the warehouses to think about deployment models where compute can be closer to the data. [27:04] And, uh, we have a deployment with Snowflake, which is part, we use a combination of what is called Snowpack container services. And really Kumo can deploy as a container in Snowflake's compute pool. So from a data scientist point of view, we're also a native app in Snowflake. So you, a data scientist in, uh, an organization, let's say YouTube can go in and, uh, [27:29] click install snowflake so it's like an app on your iphone [27:33] It gets installed and then they can start writing those predictive queries. [27:37] And looking for value. And oftentimes the security team is completely OK with it because there's no data leaving the ecosystem. We have a very similar deployment model with Databricks, though in that case we manage the GPU compute and but data residency stays completely inside Databricks. [28:07] on keyboard with, you know, with Kumo quickly. But we also realized that, [28:13] Freedom. [28:14] us up a lot to not have to think about security compliance governance [28:20] And let the data warehouses, as they're already building all of the tools and technology for, you know, for management of data.

[28:29] Let it stay there. Let it be managed there. But Kumo just, you know, talks to data directly sitting inside the warehouse. [28:37] So you talked about relational versus graph data, and relational data is kind of how many of our brains have been taught to think. Yes. [28:45] And we think about things in spreadsheets. Oftentimes we might go down and say, if we have a series of AI videos, you have them as rows and then you have some descriptors of them as columns. But really, when you start to see things as graphs, which. [29:01] I did, frankly, back in the day around Yuri's time as a professor. [29:07] I think everything starts, you can start to see everything as a graph. It's the most general data type. And when you start to see things as graphs, it's actually kind of how our brain thinks. Yes. Hey, here's a video. And that has a pointed... [29:18] characteristic that some other part of the graph, which is connected to another part. How do you ingest all of this relational data, which is the way that the world has been run for the way computers have been run for 50 years and put them into a graph structure? Sounds like a very heavy lift. And doing that inside of Snowflake and Databricks is probably pretty hard. [29:39] Of. [29:41] I want to say that's part of the magic of Como, right? And that was the friction that prevented graph learning from taking off and it staying within. And, you know, the big companies. Yes, exactly. The few people who could hire these individuals. But really, it is a question of we have a unified schema that, you know, that's the graph schema.

[30:11] looking at the relational schema, we're able to identify what the entities are. So in that YouTube example, it's a video ID, it's a user ID, it may be a channel ID, and so on. And often those are primary keys. And then it's a lot of, I want to say, SQL-like code that runs under the hood and it could [30:35] or I want to say spark-like code that runs under the hood that converts this data to the graph format. I see, I see. It makes sense. And beyond that, once we get to the graph, there's an edge index. So we store all of the edges in a very proprietary and compressed format. And then we distribute out the nodes. [31:01] Okay, because now... [31:03] Because we realized that edges... To GPUs. To distribute them to GPUs or to... To CPUs. Because we wanted to keep costs low. So we keep the... We only reserve the GPUs for training. [31:16] Right. So when we are doing the learning. [31:19] But we store the edges in what we call the graph engine. And then we have a column store where we store the features. So we can bring in arbitrary features that represent the users, right? So everything about Sonia that we can infer, [31:38] We're not constrained by memory. That just horizontally scales. Everything on the CPU machine horizontally scales, and we're only using the GPUs for message passing.

[31:49] Cool. Yeah. Amazing. In fact. Yeah. So that, uh, that, uh, that has also reduced costs for our customers and they're often surprised that we can run graph learning at the scale that we do, um, [32:04] at the costs that we do. [32:07] You've made the comparison to large language models a couple of times. Yeah. I'd be remiss not to ask, what are the connections between your graph world and the LLM world? And are there synergies between the two? Absolutely. What a great question. And so many synergies. So let's take the example of this podcast, which will get generated. It's going to get transcribed by an LLM. It's going to, it's, uh, [32:35] So you have all of the summaries, you have all of the... [32:41] the semantic information that will come from the large language models, right? A [32:47] Graph neural network can actually take [32:51] All of those features that [32:53] the semantic representation that is inferred for this particular video as a node feature. [33:01] And what the GNN is learning is it's learning across all of the interactions that one may have. [33:10] Now, let me give you another example. And this. [33:16] We have a demo of this up on our website or on our LinkedIn channel. But an example would be a lot of people think of the LLM revolution as creating chatbots. OK, so let's say you come to a clothing store and you are searching for yellow summer dresses.

[33:35] So you search Yellow Summer addresses. All the time. Yes. And you're not a logged in user. And the LLM is going to probably get you a really good set of... [33:49] things that look like yellow sum addresses. But if you were a logged in user, [33:55] and we knew all of that information about the kind of interactions that you'd had in the past, we can actually use Kumo's predictions. [34:05] Jewel. [34:07] Inform the LLM, so think of RAG, [34:10] And think of Kumo. [34:12] predictions as a [34:16] feeding a RAG algorithm to ground its truth to be closer to what is personalized. So you can do that as well. So there is the bringing in features, but then there is also the complementary because Kumo brings you all of that personalization based on all that behavioral data that the app has, which the LLM doesn't take into consideration. [34:40] You mentioned RAG, and we've talked about graphs. Graph RAG is having a moment in AI right now in general. Thoughts on Graph RAG, which is different from our approach at Kumo, but thoughts on Graph RAG, and then also how it differs from using a graph neural network to do certain inferences tied to some sort of RAG. Yeah. So Graph RAG is a lot closer to what we just talked about. But many organizations may have knowledge graphs.

[35:10] Thank you. [35:11] And that's another entire field of studies in graphs. Think. [35:17] medical domains, for example. You have all of your insurance codes and how the insurance codes connect with each other. You have symptoms, you have all of that. There's a lot of knowledge bases sitting out there with interconnected nodes. GraphFrag allows you to ground your LLM output in [35:44] the answers that are answered from these kind of knowledge graphs, right? So instead of going to a search index, so you can think of RAG as going to a search index, a knowledge graph, a recommender system like Kumo. [35:59] and making the LLM output more or hallucinate less. [36:07] I want to ask about explainable AI. One of the things we've been discussing in prior episodes of the show is, you know, these LLMs, will we ever be able to understand how they think? And I remember the anthropic results were really interesting. How do you think about explainability as it comes to Kumo's models? [36:25] That's such a great question, Sonia, because for the kinds of problems we work with and the customers that we work with, this was another area we had to. [36:35] actually develop a solution because we have customers in insurance and healthcare, and they often need to understand why a recommended output was recommended to them. You want to know that we didn't, the model didn't over-rotate on, you know, race, color, ethnicity, and so on and so forth. Right. And so it became important.

[37:03] table stakes for us to actually solve this problem. And at Kumo, we've innovated by actually developing an algorithm, which after the training part of the algorithm, looks at the graph and looks at the gradient algorithm and can come down at the table level to say these were the tables [37:33] that we can even come at an instance level and predict, you know, here's an instance, the score, why Sonia was recommended that video is high because of these specific features. Wow. So it was stable stakes just given the domain we were going into. [37:53] Yeah. Yeah. So we talked a lot about AI and graph learning. You have been in the space for a long time. Can you tell us a little bit about what you developed at LinkedIn and specifically what [38:04] AI growth was at LinkedIn? Maybe if you can, how graph neural networks help there and the types of challenges that you dealt with really, really large scale operationalizing AI? Yeah, that's a great question. So I joined LinkedIn. [38:20] just a couple of years after the IPO, and AI was making its way into various products. [38:29] I joined the growth team. [38:32] And the team I first led was the People You May Know team.

[38:36] And People You May Know is all about graphs. It's about large scale graphs and about antics. [38:49] The amazing thing about LinkedIn was how closely tied people you may know because it's a social network was to our core consumer metrics. [38:59] So, [39:01] I had come to LinkedIn as an AI researcher, and I suddenly found myself responsible for a [39:10] one of its core KPIs, which was sessions and monthly active users. And by then, I'd also started owning notifications, which is a huge part of the growth ecosystem at LinkedIn. And along the way, we had to... [39:31] start operationalizing AI. And before MLOps became a word, we were actually thinking about, hey, [39:40] How do you? [39:43] From the time when you deploy a model, how do you measure, how do you A-B test, and then how do you maintain a model in production? We would see models degrade in production. We would see our... Why is that, by the way? Why did you see that so frequently? If the graph... [40:03] wasn't losing nodes or edges. [40:05] Why would it degrade over time? Because depending on your business problem and the kind of behavior change, right? People behave on LinkedIn in the new year very differently from summer break, right? So creating those pipelines which do auto training. Yeah, absolutely.

[40:27] Those all those all became very important. And then when you talk about scale, that was an interesting problem as well, because I started at LinkedIn when we were, I think, 400 million members and then it was rapidly growing. So that's when we had to start thinking about infrastructure and the fact that we. [40:50] The CPU-based algorithms, we can't just keep horizontally scaling them. So what would be more efficient ways to run AI models in production? [41:02] um, [41:04] Graph neural networks now at LinkedIn. And I want to say there's been an amazing team that took it forward after I left as well. It took them about four to five years to build and many, many, many engineers. And but now it powers everything from the ads to the feed to jobs and so on. They published a paper recently about it. [41:34] at Pinterest. All those are massive scale, also really sophisticated. That can be, I think, intimidating for smaller companies that have problems and say, wait, this is a champagne problem. This is what the hyperscalers of hundreds of millions of users have, and we don't have nearly the same problem. Is that true? And what kinds of companies are not a good fit for graph learning? [41:57] That's a great question. So I think there are two things. The first question is the reason why we ended up inventing predictive query language, because what we needed to do was create a platform that was super easy to use.

[42:15] Right. And many of the other companies had few data scientists and large number of [42:22] potential avenues where they wanted to bring AI. [42:27] oftentimes what we would get is, hey, we would love to be a LinkedIn and Airbnb or a Pinterest. [42:33] but we can't put so many people to it. So giving them that insight, [42:38] easy to use interface and giving them that managed infrastructure at scale actually lets us get in. But that's it. When is Kumo not a fit? [42:49] Kumo is not a fit if you're so early on that you haven't figured out your data landscape. So sometimes we'll talk to customers who are super excited about Kumo. [43:01] but they haven't figured out how to measure the value of AI. Or sometimes we'll talk to customers and they're still in spreadsheets and they're moving to one of the warehouses. So we'll say, you know, [43:16] get the data layout settled. It's like [43:20] Building a city, right? You've got to have your roads in the foundation first and then the vehicles come on it. So we'll wait and customers come back, you know, in a year or so. But there's no category or type of problem. It's more a data sophistication. Exactly. I see. Yeah. [43:36] But you don't have to be so far along the sophistication curve. You don't have to be an Airbnb LinkedIn. Yeah, it's table stakes. You know, certain KPIs that can be optimized by an algorithm. Yeah. You can quantify certain things, A. And then B, you have access to that data and something that you can plug into like a data warehouse. Yeah.

[43:54] And the way I look at the evolution of an organization in data is often that of [44:01] They'll... [44:02] You, of course, have to know what your product market fit is. [44:06] After that, you start figuring out your data ecosystem. [44:10] Thank you. [44:11] You build your data ecosystem for analytics because now you've built a product. You've got to start measuring what that product is doing, what it's starting, what the behavior is. And that's when leaders usually start thinking about AI, which is, OK, now I know how to query the past. But I now need to start bringing in AI to instrument the change that I need in the in the ecosystem. [44:38] Hmm. [44:39] I'd love to close with some questions about your vision for the future. [44:43] Maybe, you know, you've been in AI for a long time. You mentioned you were in NLP before before BERT was a thing. Yeah. What are you most excited about in AI most broadly? [44:53] Oh. [44:54] I think I'm most excited. [44:56] very broadly about the productivity gains it's giving all of us. I mean, Kumo is one part of it, but just how we write documents or how we, or think about health, right? Like if health improves, productivity improves. For example, if you're just using one of those health apps that do monitoring, but nudge you to for behavioral change, that is better health is better productivity.

[45:26] What I'm most excited about is how we're going to evolve as a human race with [45:33] All of this productivity gains. [45:36] What about technically? What, what, [45:38] features or approaches or algorithms or venues you think are going to be most interesting? [45:44] I've always found it. [45:45] The big innovations come at the intersection of hardware and software. [45:52] And I think while GPUs were in, [45:55] invented for graphics. [45:58] There's probably something more that has to happen on the processor side so that you can scale these graph neural networks and neural networks further, make models better. [46:12] of, [46:13] maybe less expensive. [46:16] So, [46:17] I'm looking forward to that technically. [46:20] What about your vision for the future of Kumo? What can we expect to come out of the product in the future? [46:26] So in terms of Kumo's vision, I'm actually really excited about the kinds of apps people are going to build with Kumo. We're starting to see people plumb Kumo with Kumo. [46:37] Lanchain and Pinecone and [46:42] put together apps like the one we talked about, like the, you know, a chat agent that recommends for you the yellow summer dresses, right? So I'm very excited about the top layer of applications that are going to get built on top of Kumo and what that's going to power.

[47:04] So, Hema, one of the star features about you, you're incredibly technically deep, but you also are really good at culture. If you talk to anyone at Kumo, there's basically no regrettable turn ever. And you guys hire some of the best PhDs in the world in ML and certainly in GraphML. How do you do that? What have you done to make the Kumo culture exceptional and to have so much retention within your team? [47:26] I was at a leadership training once and we had to think about what our true north was. And I realized that, and the true north concept defines your true north, which is a value as who you are, but what, you know, the kinds of problems you solve either in your work and what you bring to the table. And for me, it's always about empowering people to do more than what they think they can. So it's common to empower people to do what they, [47:56] to get to full potential. But it's those aha moments, like, wow, I built this. So you hire a smart team. [48:04] You get them. [48:06] I think good managers step away from, [48:09] but have their eye on how the team is operating. [48:13] And you get people to... [48:17] Innovate. [48:18] get people to own what they're building. So just see that vision. So you want to be able to hire people, whether it was LinkedIn, where the value was economic opportunity, or at Como, where the value is about... [48:32] Oh. [48:33] Building a AI platform that makes AI so easy to use. It's about when you bring smart people together that rally together on the same value. That's when magic happens. And my job is to just let the magic happen.

[48:48] Why is that passion around relational data? You could have done, we've talked about this sometimes in the past, you could have used graph learning as a different type of architecture to do language models, or you could have done graph learning to do any sort of. [49:05] Any sort of AI, right? Once you've figured out at scale how to do the generalization, why can't you do the specifics by taking this big marble block and carving away all the nodes and edges until you get to a superior architecture? We decided to do it on relational data. Why is that? Relational data usually is not the most exciting thing in the world for most people. [49:22] Yeah. But it is your life passion. [49:24] because nobody else was doing it. And there's so much data in relational format. And that was such a pain in our past jobs. [49:34] So, [49:36] I feel like there's magic happening in the core area of NLP where, you know, I'm happy to see that revolution and all the investment that's happening there, people, money and so on. [49:49] There's this whole workload that's out there, a whole set of data scientists that work. [49:56] work on those workloads? How do we bring that magic to them? So it's really about the opportunity to, [50:05] and the past pain that each of us saw in our previous jobs. [50:11] I have one last question for the young Constantine's out there who are watching this episode on YouTube. What advice do you have for aspiring AI engineers who want to really make a dent in the field in the future?

[50:24] She has a specific yellow dress recommendation. I would actually say tools come and go. Languages come and go. I know there's a lot about learning Python and, you know, taking the class on the latest deep learning. But I would say don't skip your probability and linear algebra classes, because when it whatever method has been there in the last several decades, it's always come down to. [50:52] core linear algebra [50:54] and probability. So don't skip those classes. [50:57] Okay. And mine is, there's a lot of graph enthusiasts out there. When do graph neural networks come main stage in the AI revolution? [51:08] Best guess for Timeline. [51:09] I think we're getting there. I was at a conference called KDD recently. It's one of the biggest data mining conferences, a lot of academics, a lot of industry folks, and more than half the papers were graph neural networks. So I think we're sitting at... [51:26] That explosion, it's going to happen. [51:29] Thank you, Hema. This was fantastic. [51:31] Thank you, Sonia. And thank you, Constantine. It was lovely being here. [51:35] Music.