Montreal MLOps Meetup Kick-off

Hello, everyone, to our first Montreal MLOps Community event!

This will be an in-person event, and it'll take place at Temps Libre co-working space in Mile End.

The goals of this and future events are to get to know people working in the MLOps field, share knowledge and find collaborators/future colleagues.
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The agenda for this inaugural meetup will be as follows:
16:30 - Doors open
16:45 - Introduction
17:00 - Presentations
--- "Power up your ML projects with MLflow" by Jean-Michel Daignan
--- "Introduction to Kedro: production-ready data science pipelines" by Vinh Nguyen
17:50 - Coffee break and networking
18:10 - Presentations
--- "Power your ML models with quality data using Meltano & DBT" by Stéphane Burwash
--- "CI/CD for Machine Learning with CML" by Alex Kim
19:00 - Raffle and wrap-up
19:15 - Drinks and food at Siboire Saint-Laurent (5101 St Laurent Blvd)

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Power up your ML projects with MLflow
Join Jean-Michel as he explores MLflow, the machine learning management package. He’ll cover the main features, share his practical experience using MLflow at Ubisoft and discuss the limitations he faced and how he overcame them. Get ready to learn about streamlining your machine learning pipeline with MLflow.
About Jean-Michel:
He is a data scientist at Ubisoft, where he operates machine learning projects in production like recommender systems and helps develop and support the internal ML platform. He also runs the blog The Odd Dataguy, sharing his side projects in data and machine learning.
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Introduction to Kedro: production-ready data science pipelines
Do you want to reduce the time between development and code production-ready? Come to learn Kedro, a fantastic open-source tool to create reproducible, maintainable, and modular data science code. Vinh will cover Kedro’s concepts and features that answer the need for production-ready data science pipelines. He’ll share his practical experience in optimizing the development of a data science project.
About Vinh:
After two years as a data scientist in France, I switched to MLOps to focus on best-serving data science models. As the first data scientist in my previous experience, I had the opportunity to work on ML model deployment. At Potloc, I aim to build a strong ML Ops stack to safely and easily deploy an increasing number of ML models.
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Power your ML models with quality data using Meltano & DBT
A machine learning model is only as good as the data that backs it up. But getting the right data in the right format regularly is harder said than done.Join Stéphane as he gives a crash course on how to get your first data pipeline up and running using Meltano & DBT. He will tackle how to Extract, Load and Transform your data in order to power all of your ML models, as well as discuss important concepts such as Orchestration and Data Quality.
About Stéphane:
With a background in data science, Stéphane is currently the lead Data-Ops engineer at Potloc. His responsibilities include creating data pipelines, integrating new sources, monitoring data integrity, and understanding the data needs of downstream users. He aims to bridge the gap between the abstract world of data and the concrete world of business.
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CI/CD for Machine Learning with CML
Applying DevOps methodologies to machine learning (MLOps) and data management (DataOps) is increasingly common. This means resource orchestration (provisioning servers for model training), model testing (validating model inference), and model deployment to production, as well as monitoring & feedback.
In this talk, I’ll present CML, which is an open-source tool that enables all this by integrating with common CI/CD platforms.

About Alex:
Alex Kim is an MLOps Engineer. His background is in physics, software engineering, and machine learning. In the last few years, he became increasingly interested in the engineering side of ML projects: processes and tools needed to go from an idea to a production solution.
He occasionally teaches online courses on the topics of Data Science, Machine Learning, and MLOps.
alex000kim.notion.site
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