Data Science & AI: Getting Buy-In and Demonstrating ROI | PagerDuty’s Sanghamitra Goswami

**Sanghamitra Goswami** (0:00)
Each phase should have a deliverable. It might not be a specific product or anything. It is okay to be imperfect, but let's have a goal.
And when we have a goal and when we have a plan at each phase, then big foundational work might seem achievable. Although you cannot see an ROI at one and two, but since I know, I can give you that ROI back at phase six. So this is very important. In that case, you'll be more convinced than me going to you and saying that, hey, I don't really know how to go to phase six, but we need to do phase one.

**Conor Bronsdon** (0:36)
How can you drive developer productivity, lower costs, and deliver better products?
On June 20th and 27th, Linear B is hosting a workshop that explores the software engineering intelligence category. We'll showcase how data-driven insights and innovative workflow automations can optimize your software delivery practices. At the end of the workshop, you'll leave with a complimentary Gartner market guide to software engineering intelligence and other resources to help you get started. Head to the show notes or linearb.io events to sign up today. Welcome back to Dev Interrupted. I'm your co-host, Conor Bronsdon. Today, I'm being joined by Sanghamitra Goswami, Senior Director of Data Science and Machine Learning at PagerDuty.
Sanghamitra, thank you so much for joining me.

**Sanghamitra Goswami** (1:18)
Thank you, Conor, for inviting me.

**Conor Bronsdon** (1:20)
Honestly, it's my pleasure. I think we could really benefit from your expertise as someone who has such a deep understanding of the approach that data scientists have taken to developing AI models. And, I mean, frankly, AI is all the rage right now, right? Everyone's talking about it. Everyone has opinions on what it is.
And so it's important that we level set with the audience a bit and have the opportunity here to pick the brain of a data science leader like you and understand how engineering teams can translate this and leverage AI models or LLMs in particular within their org. So let's maybe unravel some of the strategies that champion the role of data science, machine learning, AI teams, and help our audience understand how to navigate this emerging future and ever-expanding landscape.
Why don't we talk a bit about the history of LLMs, what they are, and why they're so important?

**Sanghamitra Goswami** (2:12)
You know, Conor, it's been a crazy time now.
Nine months back when Chad Jubidy came out, PagerDuty leadership, they told me, Mitra, we need to do something with LLMs. And it's crazy the way the world is saying that, hey, we want to do LLM, let's have a feature that uses LLMs. So what are LLMs? Large language models, they leverage foundational machine learning, AI, deep learning models to understand natural language and to give answers as so they can talk to us like a bot. If you look at the history of NLP, it's based on NLP, it's based on the NLP models that we have built out. If you look at the history of NLP, in 1948, Shannon and Weaver, the first paper came out. And during that time, we didn't have the computer storage that is possible now. We couldn't really have a lot of computational power. So it was not possible to always run this large language models because they required large amounts of text.
However, from that start, where Shannon and Weaver were in 1948, if you fast forward, I would actually mention there is another milestone where the transformer architecture got introduced. And attention is all you need is the paper. So if I look at these two milestones and how the landscape has changed, the computational power, GPUs, so with everything in mind, now is the perfect time that we can all reap the benefits of years of research and computational power and engineering in that sense. So this is the perfect time.

**Conor Bronsdon** (3:52)
Absolutely. It's really interesting to think about how even just a few years ago, before we realized that we could paralyze processes within AI model development using GPUs, there just wasn't this speed of development that we've seen on AI models today.

**Sanghamitra Goswami** (4:06)
I know.

**Conor Bronsdon** (4:07)
So I'd love to kind of talk about how teams can actually leverage AI or LLMs within their tooling.
What would be your advice to engineering leaders who are thinking, hey, I want to start using AI to extend the capacity of our product. How should they kick off?

**Sanghamitra Goswami** (4:24)
I think, let's start with AI, just AI. Someone wants to do AI with their teams, okay? That is a huge challenge, because if you look at all the leaders, in the industry, how do we see if something is successful in the industry? We have to get some ROI with all the endeavors, right? And data science, AI, it's an experiment. So when we start building it, it is always not clear how this is going to show up.