Name: PyData Montreal Meetup #32 (in-person | en personne)
Start: 2025-11-26T18:00:00-05:00
End: 2025-11-26T21:00:00-05:00
Location: Potloc HQ

🍂☕ As fall winds down and winter whispers get louder, there's no better time to cozy up indoors with your favourite warm beverage and join us for another exciting PyData meetup! We're wrapping up the season with two fantastic talks on November 26th at the Potloc offices 🎉, co-sponsored by the amazing team at Infostrux 🤝.

**AGENDA**

* 18h00 - Open doors
* 18h10 - Introduction
* 18h20 - Talk #1
* 19h00 - Break
* 19h15 - Talk #2
* 20h00 - Networking
* 20h45 - End of event

**TALKS**
**Talk #1: Your Model is Confused (And That's a Good Thing): An Intro to Active Learning**
By [Frédéric Branchaud-Charron](https://www.linkedin.com/in/fr%C3%A9d%C3%A9ric-branchaud-charron-5387b8ba/?utm_source=share&utm_campaign=share_via&utm_content=profile&utm_medium=android_app)
Description: TBD

**Talk #2:** Libero: The Insurance Broker’s Best Ally
By [Catherine Paulin](https://www.linkedin.com/in/catherine-paulin/) & [Etienne Boisvenue](https://www.linkedin.com/in/etienne-boisvenue-23a160134/)
Description:

When a team of insurance brokers receives more than **500 emails per day** from clients, it quickly becomes difficult to keep things organized and make sense of it all. That’s where **Libero** comes in: a solution that **summarizes and classifies** all incoming emails. Beyond that, Libero also **categorizes them properly within the client database (CRM).** All of this, which brokers and their assistants used to do manually, is now **fully automated**, freeing up a tremendous amount of time every day. Through this presentation, we want to bring you **into the heart of Libero’s design and the key decisions made during its development:** its architecture, the challenges, the specific requirements of the insurance industry, the solution’s evolution, and more. And most importantly, we want to open up a discussion with you, the community, around this question:
\- How do we build and deploy AI solutions that can actually be maintained and evolve over time?

This question is more important now than ever, with **the rapid evolution of AI solutions, products and services** that hit the market each week. The rhythm of innovation (and sometime fluff…) is astonishing! How do we continue building solutions that stay relevant and keep delivering business value? As a service provider, iuvo-ai is constantly balancing innovation with pragmatism. Every client has a different level of technical maturity, infrastructure, and internal talent. In that reality, the real challenge isn’t just getting a solution to work; it’s making sure it can live on. How do we design architectures that are flexible enough to evolve as the ecosystem changes, but simple enough for our clients to own and maintain? How do we make decisions that reduce friction when the next API version drops or when the internal IT team needs to take over? Those are the questions we wrestle with every day when bringing AI into production, and we’d love to exchange ideas and lessons learned with you 🙂

**Bios of the speakers**
**Frédéric Branchaud-Charron:** Fred has spent 6+ years shipping ML at startups like ElementAI, Glowstick, and SafelyYou, working across computer vision and NLP problems. He's particularly obsessed with Bayesian Deep Learning and Active Learning—obsessed enough to maintain Baal, an open-source library for Bayesian AL. He also occasionally advises startups & teams when not debugging Bayesian inference pipelines.

**Catherine Paulin:** Catherine builds bridges between imagination and intelligence. As co-founder and CTO of iuvo-ai, she helps organizations turn ambitious ideas into applied AI solutions that actually make a difference, from computer vision systems to generative AI and predictive models. Her journey began in research, exploring deep learning through projects that touched everything from bird song classification to spectral imaging. Over time, that curiosity evolved into a drive to bring AI out of the lab and into the real world. Before launching iuvo-ai, she led computer vision initiatives at Volta Charging, deploying models on smart Electric Vehicule chargers, and contributed to NLP innovation at Gartner. Today, she focuses on helping SMEs integrate AI into their processes combining technical excellence with empathy, creativity, and business sense.

**Etienne Boisvenue:** Etienne is a curious person. It is as simple as that. Whether it be regarding data science, product development, cooking, or quirky hobbies (no one knows why he decided to learn Morse code a few years ago...!) And his professional journey is a perfect example of that mindset: from physics, to cyber security, to finally landing in AI. He started his career at Deloitte in cyber risk management to eventually switched to a Montreal-based, fast-growing startup: Moov AI. As the third employee, he helped the company grow in many spheres: improving delivery project methodologies, leading RnD work for ML quality assessment techniques, and mentoring new talents. And history repeats itself! Today, Etienne is, once again, the third employee of a new up and coming AI consultancy company: iuvo-ai. At iuvo-ai, the objective is simple: to deliver high-quality, close-to-client services, with client's successes truly at the heart of operations. Caring is at the core of iuvo-ai's practice, and it always will be. iuvo-ai is there to help, and that's genuine.

We're excited to see you there! 🍁👋

Jean Philippe Petit-Frère

Jean-Michel Daignan

Vinh Nguyen

Stephane Burwash

PyData Montreal

PyData

NumFOCUS

Technology

Python

Statistical Computing

Big Data

High Scalability Computing

Machine Learning

Data Analytics

Data Visualization

Data Mining

Data Science

Open Source Python

Deep Learning

Data Science using Python

Data Engineering

PyData Montreal Meetup #32 (in-person | en personne)

Potloc HQ

Share

PyData Montreal

PyData Montreal Meetup #32 (in-person | en personne)

PyData Montreal

Details

Sponsors

Members are also interested in