Uploaded December 2025 | Updated September 2026, 1 week ago
ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We'd love to see you!
We were recently joined by Kevin Dalton, Senior Data Scientist at Great American Insurance Group, to chat about data science in the insurance industry, MLOps and productionalization strategies, Bayesian modeling, and career development.
In this Hangout, we explore MLOps and productionalization strategies, especially the distinction between "little P" and "big P" production. Kevin explained that "small p" production involves internal dashboards, interactive applications, or analytics reports used to support the research process and assess the business, generally having a lower risk profile. In contrast, "big P" production refers to ML models deployed for inference in core business systems, which demand strict Service Level Agreements (SLAs). Kevin noted that the industry is moving towards automated, cloud-centric, real-time inference engines, although production systems can still be as simple as running a notebook over new data.
Resources mentioned in the video and zoom chat:
🔗 The (new!) Data Science Lab on Tuesdays → https://pos.it/dslab
🔗 Bayes Rules! Book → bayesrulesbook.com
🔗 Positron link → positron.posit.co
If you didn’t join live, one great discussion you missed from the zoom chat was about the humorous observations regarding Kevin's unique professional history, with some referring to it as the "skydiving to actuary pipeline" 😂 There was some speculation about whether professional skydivers have "No fear to deploy models" and then there was a subsequent realization about all the insurance needed for a skydiving business. That tracks!
► Subscribe to Our Channel Here: bit.ly/2TzgcOu
Follow Us Here:
Website: posit.co
Hangout: https://pos.it/dsh
LinkedIn: linkedin.com/company/posit-software
Bluesky: https://bsky.app/profile/posit.co
Thanks for hanging out with us! 💛
Timestamps
00:00 Introduction
05:06 "What does it actually mean to put models into production at an insurance company?"
07:27 "How do you actually explain that to people at the company that there's this need for two different types of production?"
09:21 "Have you guys, in a large company, found any application for smaller language models in, like, production pipelines?"
10:17 "Are there any lessons from the skydiving experience that you apply to your data science work?"
12:45 "What recent innovative ML models in the insurance sectors have you brought live if you're allowed to share?"
17:33 "I'm just curious to hear from you or others in chat who spend a lot of time in R if there are strategies you found effective in bridging the gap to using Python."
20:54 "What was the main thing that got you from "who is this for?" when you first opened Positron to now using it?"
22:46 "Could you share a little bit about what your tool stack looks like at Great American Insurance Group?"
24:27 "So you actually run Posit within Snowflake. Is that right?"
26:32 "What sort of guardrails are you using that are specific to insurance to guard against drift?"
29:26 "Do you think that'll be the future when it comes to test driven development?"
30:55 "What are some of your favorite public data sources for trying to quantify and model things like geopolitical risk?"
32:07 "If you wanna learn more about using tools you mentioned for building synthetic data, where would you recommend that people start?"
34:27 "So with the AI evolving, what kind of techniques are being recently used?"
35:28 "Broadly, how is AI affecting your day to day work in the insurance industry?"
38:21 "Is there a piece of career advice that's been especially meaningful to you, whether it's something that you've heard from a mentor or you've given this advice to other people?"
40:04 "How would you advise people to get domain expertise before they've gotten a chance to do a job in that industry?"
46:19 "Which skills would you say are evergreen irrespective of the tools that you're using?"
47:51 "What are you learning about change management?"
49:31 "What does seeking mentorship mean for you?"
53:21 "Do you have any strategies or advice for keeping that statistical intuition sharp?"
54:16 "How do you partner with actuaries at work? Are there things that you need from the actuaries that make your job easier?"
ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We'd love to see you!
We were recently joined by Kevin Dalton, Senior Data Scientist at Great American Insurance Group, to chat about data science in the insurance industry, MLOps and productionalization strategies, Bayesian modeling, and career development.
In this Hangout, we explore MLOps and productionalization strategies, especially the distinction between "little P" and "big P" production. Kevin explained that "small p" production involves internal dashboards, interactive applications, or analytics reports used to support the research process and assess the business, generally having a lower risk profile. In contrast, "big P" production refers to ML models deployed for inference in core business systems, which demand strict Service Level Agreements (SLAs). Kevin noted that the industry is moving towards automated, cloud-centric, real-time inference engines, although production systems can still be as simple as running a notebook over new data.
Resources mentioned in the video and zoom chat:
🔗 The (new!) Data Science Lab on Tuesdays → https://pos.it/dslab
🔗 Bayes Rules! Book → bayesrulesbook.com
🔗 Positron link → positron.posit.co
If you didn’t join live, one great discussion you missed from the zoom chat was about the humorous observations regarding Kevin's unique professional history, with some referring to it as the "skydiving to actuary pipeline" 😂 There was some speculation about whether professional skydivers have "No fear to deploy models" and then there was a subsequent realization about all the insurance needed for a skydiving business. That tracks!
► Subscribe to Our Channel Here: bit.ly/2TzgcOu
Follow Us Here:
Website: posit.co
Hangout: https://pos.it/dsh
LinkedIn: linkedin.com/company/posit-software
Bluesky: https://bsky.app/profile/posit.co
Thanks for hanging out with us! 💛
Timestamps
00:00 Introduction
05:06 "What does it actually mean to put models into production at an insurance company?"
07:27 "How do you actually explain that to people at the company that there's this need for two different types of production?"
09:21 "Have you guys, in a large company, found any application for smaller language models in, like, production pipelines?"
10:17 "Are there any lessons from the skydiving experience that you apply to your data science work?"
12:45 "What recent innovative ML models in the insurance sectors have you brought live if you're allowed to share?"
17:33 "I'm just curious to hear from you or others in chat who spend a lot of time in R if there are strategies you found effective in bridging the gap to using Python."
20:54 "What was the main thing that got you from "who is this for?" when you first opened Positron to now using it?"
22:46 "Could you share a little bit about what your tool stack looks like at Great American Insurance Group?"
24:27 "So you actually run Posit within Snowflake. Is that right?"
26:32 "What sort of guardrails are you using that are specific to insurance to guard against drift?"
29:26 "Do you think that'll be the future when it comes to test driven development?"
30:55 "What are some of your favorite public data sources for trying to quantify and model things like geopolitical risk?"
32:07 "If you wanna learn more about using tools you mentioned for building synthetic data, where would you recommend that people start?"
34:27 "So with the AI evolving, what kind of techniques are being recently used?"
35:28 "Broadly, how is AI affecting your day to day work in the insurance industry?"
38:21 "Is there a piece of career advice that's been especially meaningful to you, whether it's something that you've heard from a mentor or you've given this advice to other people?"
40:04 "How would you advise people to get domain expertise before they've gotten a chance to do a job in that industry?"
46:19 "Which skills would you say are evergreen irrespective of the tools that you're using?"
47:51 "What are you learning about change management?"
49:31 "What does seeking mentorship mean for you?"
53:21 "Do you have any strategies or advice for keeping that statistical intuition sharp?"
54:16 "How do you partner with actuaries at work? Are there things that you need from the actuaries that make your job easier?"
