How to QA your ML models

Building a robust machine learning model is not an easy task. After all, most POCs don't make it into production. And even if they make it into production, you still need to monitor its performance.
How can you build performant, tolerant, stable, predictive models that have known and fair biases? How can you make sure your models yield their value over time and stay performant after your team has deployed them? What are the current practices of model validation (or lack of), how are they flawed, and how could we improve them?

Simon Dagenais from Snitch AI will go through the reasons behind using an efficient validation framework that goes beyond the common metrics used by ML practitioners and why these tests matter when building high-quality models.

Agenda:

3:45pm - 4:00pm: Arrival & Networking
4:00pm - 4:15pm: News & Intro
4:15pm - 5:15pm: How to QA your ML models
5:15pm - 5:30pm: Virtual Snack & Networking

About the main speaker:

Simon Dagenais is the Lead Data Scientist at Snitch AI, a machine learning validation tool. Before working on Snitch AI, Simon was a data scientist consultant at Moov AI, the parent company of Snitch AI. During his time as a consultant, he built and deployed custom ML solutions to solve business needs at companies like DRW, Société de Transport de Montréal and Cogeco. He now aspires to solve problems that data science teams will encounter during the course of a ML project cycle. Simon obtained an M.Sc. in economics from HEC Montreal. He frequently speaks in conferences, panels and meetups.

Logistics

This is an online event. Participants will receive the link to the video conference after they register.
The schedule above is using Eastern Daylight Time (GMT-4).