On this episode of The Six Five – On The Road Partner Edition, hosts Daniel Newman and Patrick Moorhead welcome Cisco’s Dev Bapat, Head of AI ML Data Products, and Yatish Joshi, Technical Leader for a discussion during MongoDB’s .local NYC event.
Their discussion covers:
- The impacts driven by AI and Machine Learning for Cisco’s customers and partners
- Yatish shares his insights on scalability and the requirements to meet Cisco’s existing and future demand
- Dev shares his strategy for prioritizing with so many competing requirements and timelines
- Yatish gives us perspective on how to balance a dynamic innovation roadmap with incoming customer requests
You can watch the full video here:
You can listen to the conversation here:
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Patrick Moorhead: Hi, this is Pat Moorhead, and The Six Five is live and on the road at MongoDB.local here in New York City. And as you can hear from this background, we’re right on the show floor. It’s exciting. Big announcements, big conversation. But I’m here with my bestie, Daniel Newman, so it’s a good day.
Daniel Newman: Yeah. What’s this, one of 30 events?
Patrick Moorhead: Yes.
Daniel Newman: MongoDB Local. Instead of taking it that big, we’re going to do one massive event. We’re going to take the event to you. It’s kind of a cool, different approach to events. And thankfully for you and I, we don’t have to do all 30. Not that I-
Patrick Moorhead: Exactly.
Daniel Newman: … wouldn’t mind getting to some of those locations, but we do travel a decent bit. But the energy here is really good. And the topic in focus has been a lot about AI, and I think we’re going to talk about that here once again.
Patrick Moorhead: It is. The great part is we also research Cisco a lot, and we’ve been researching the company for over a decade. But gentlemen, thanks for coming on the show.
Dev Bapat: Thank you for inviting us.
Patrick Moorhead: Dev, great to see you. Yatish, thank you for coming on the show.
Yatish Joshi: Glad to be here.
Daniel Newman: Yeah. So Dev, let’s start with you.
Dev Bapat: Sure.
Daniel Newman: We’re here at MongoDB, but Cisco has to have AIML on its mind. Of course, recently it’s Cisco Live.
Dev Bapat: Yes.
Daniel Newman: We heard from Chuck Robbins, and we heard from a number of your peers and executives talking a lot about it. But I would love to just get your overall take on what are the exciting, the important opportunities that you’re focused on as one of the leaders in the AIML part of Cisco’s business for Cisco itself?
Dev Bapat: Yeah, sure. So let me actually break it down. And your right, AIML is a hot topic right now. And that is actually amplified by generative AI a lot, which has been out there for a while, but now suddenly it’s come to prominence.
Daniel Newman: Exactly.
Dev Bapat: So, there is a lot of work that’s happening at Cisco, some of which you probably already covered in Cisco Live. There is a lot of work happening in our area as well. Cisco has its own business entities and a centralized group that runs AIML. So, if I break it down initially into security and productivity, and then some around internal, the way I look at it is, security you guys probably looked at it a lot on Cisco Live. There are a lot of initiatives around intrusion detection, monitoring detection, as well as corrective actions. There are companies that Cisco also partners because, as you guys are probably aware, there’s a lot of open source development that happens, and sometimes open source also leads to code [inaudible 00:02:44], so there are companies that we partner with that test out on those codes so that the code that is deployed is also secure. So that’s what we call as preventative mechanisms that are in place. So that’s a whole lot on the security side.
On the productivity side as well, you probably know the full stack observability where Cisco tries to optimize notes for data transfer. You also probably know the work that we do in collab around speech to text, around translation. Similar to that on open source, Cisco also partners with companies around improving the core quality itself, so that whatever is deployed is actually robust enough. Not to mention, we also have a lot of investment in generative AI, especially as we guide our Technical Support Engineers for our product-specific information and product-specific guidance. That’s all on the external side, but we apply a lot of, because Cisco is a B2B company, we apply a lot of AIML on the internal side as well, right from identifying data patterns and data observability all the way to predicting and targeting the right customers for Convergent. And we also partner with customers to make sure that they make better use of our products for the price that they’re already paying.
So, a lot of exciting things are happening across the entire Cisco organization, across Engineering, across Commercial.
Patrick Moorhead: Yeah. By the way, I love consistency, because if I had to play back before interview, I would’ve said, “Hey, these are the three areas that we wrote about in terms of Cisco Live and AI,” and I’m just, I don’t think you need to be a futurist to see where this is going. On the security part, it’s going to be this AI versus AI. Spy versus Spy. I am super excited about the future of collaboration. Specifically, though, in the call center, and how that can transform that experience.
So, Yatish, it’s easy for us to get up as pundits. Dan and I talk about all these great futuristic things that could happen and maybe should happen. It plays a part in industry. You actually have to build these things. And I’m curious, when it comes to scalability and unique requirements for AIML related to what you do, can you talk a little bit about that? What are some of those?
Yatish Joshi: Sure. So, in the secure firewall side, my team is responsible for threat intelligence ingestion, and making it actionable on the product. So, we deal with on-prem hardware devices, we develop for the cloud as well. And, it’s a challenge. It’s tough to scale not only on-prem, but also on the cloud. So, sometimes you’re thinking about scaling vertically by adding more resources, making the system beefier. In other cases you’re like, “Okay, we need to process a whole lot more data, so should we process in Palo? What are the costs, what are the challenges to do that? So, it becomes more of a… You try to lean on the process and define it so that you have the clear requirements as to what you are trying to build, what’s the data that you’re trying to ingest? And, make sure that, you know, try to meet some kind of benchmark that’s set for you. So if you can meet that benchmark and make sure your entire team is onboard with the design, the architecture, and make sure that, how do you make sure things are scalable? You test them.
So you need that automated CICD pipeline in place where you make a change, you can test it right away and see how that one change affects my entire system? And, that’s where through continuous iterative testing and improvements, you can keep making your systems faster, more scalable, more efficient, and handle change because that’s the only thing that’s constant in this world.
Patrick Moorhead: Yes.
Daniel Newman: So Dev, leading AIML products for all of Cisco. I imagine you have a stack, I would say of paper, but I hope it’s not. But your inbox is probably pretty full, I’d guess your WebEx has quite a few requests coming in. All of what’s possible. All the products that can be built on top of observability, products can be built on top of collaboration. You’ve got intent-based networking, you’ve got security as a booming business. How are you dealing with prioritization? Because no matter how big a company you are, you still can’t do it all at one time. And I’m guessing all the business units feel that their priority projects are the most important. And so how are you juggling this with balancing the product with equal importance?
Dev Bapat: Yeah. So one thing is, our group primarily focuses on customer experience. There are different groups that actually manage AIML products for different business entities. So we are more on the customer experience and renewals. So yes, we have enough demand, I would say, but at the same time, one thing that I learned is, there are grounds for experimentation. Generally, we have certain criteria that we follow around it’s not just a classic two-by-12 impact versus ease of use. There are other things that we also look at. One is, the end goal is always how do I make sure that I have a full stack digital product? It’s not just about AIML. It’s about AIML embedded into a product, and a product embedded into a business process. So, that’s the ultimate goal.
And then if you work our way back from that last mile, we make sure that whatever program we choose or whatever product we choose, and we don’t succeed every time, but when we do, we make sure that there is enough support from the senior management, there is enough adoption from the middle management at the last mile. That’s on one side from usability. From a technical perspective, it is a scalable solution that we can actually scale to a full stack. And actually deploy into the field. Those are the two major aspects that we look at, and of course we have to have the right resources and capability, that goes without saying. So, it starts with impact, it starts with experimentation, but we rigidly follow those criteria. Otherwise, the risk that we see in AIML is, it can become a cottage industry of algorithms.
Patrick Moorhead: For both of you, I have utmost respect for anybody who runs products.
Dev Bapat: Yeah.
Patrick Moorhead: I did this mid-career, and it was the toughest job that I had. I manage Engineering teams, P&L, and also the business. It was tough.
Dev Bapat: Yeah.
Patrick Moorhead: I’m curious, Yatish, you have all of these different people wanting different things, and then you have different timescales, and then you have different quality levels that you can move knobs back and forth. You have real-time customer feedback like, “Hey, I need this changed,” but you can’t have this constant and perpetual, you have to lock in on a roadmap. How do you manage all those competing priorities? And I don’t know if you would call that product development approach, maybe a product management strategy. I don’t know, but how do you handle that?
Yatish Joshi: That’s a good question.
Patrick Moorhead: You’re like, “Welcome back to my life here.”
Yatish Joshi: I know, so I talk with customers a lot, right?
Patrick Moorhead: Right.
Yatish Joshi: So, frankly at this point I love customer requests because they’re the guys who are actually using the product, so they find ways to break stuff that you never thought of during design and dev. And you’re like, “How did this happen?” So, it’s always challenging when you work your way backwards. And, the main question that comes to my head is, Why? How did you break it, and how can we help you?”
Daniel Newman: Yes.
Yatish Joshi: Because, if you can answer that question, some of those requests are really simple. You can be like, “Hey, I can do this in one day. Let’s just do it right away and we are done.” Some are more complex requests and more effort than planning and then you’re like, “Okay, does it fit in our roadmap? If it does, maybe we can get it out in the next release,” for example.
Daniel Newman: Yes.
Yatish Joshi: And, sometimes you do have to say no because there are some requests that just would adversely impact other customers, and they don’t really fit with what you’re trying to build and develop. So, saying no is stuff, but I’ve had to do it on multiple calls. But having said that, having customer requests is a good thing, because that’s the way you get… Sometimes you get new ideas too. For example, because they have new use cases that you never thought of and you’re like, “Wow, hey. This is something good,” and the roadmap has to be flexible. So you have to be flexible and adapt. And if you can do that, that’s the way you also innovate and keep customers happy.
Daniel Newman: No can be powerful.
Yatish Joshi: Yes.
Daniel Newman: And we know that, whether it’s leading people, raising your children, you got to learn to put no to use frequently.
Patrick Moorhead: Yeah.
Daniel Newman: You don’t do it often.
Patrick Moorhead: It’s easy to say yes to everything. The hard part is to say no.
Daniel Newman: It’s hard, but it’s not always right. Steve Jobs was infamous for saying, “Yeah, thank you customer, but I’m still going to do this.” There’s always that kind of in between, because you have a lot of innate knowledge when you’re running product about what it can and can’t do. Customers want everything, and of course every customer thinks their thing is the most important thing. It’s finding that balance, which is an everyday task for both of you.
We’ve got to close up here. I want to thank you both very much for joining us here on The Six Five. Hope to have you guys back again on the show sometime soon.
Yatish Joshi: Sure, thank you.
Dev Bapat: Thanks for inviting us. Thank you.
Daniel Newman: All right, everyone. Here we are at MongoDB.local New York City, 2023. This is the Six Five on the road. Hit that Subscribe button, tune into all the episodes. We had eight of them here, and we hope you join us For those, and for all the other Six Five shows. For Patrick, for myself, time to say goodbye. Stay tuned with us. See you soon.