Montreal MLOps Meetup #2

note to access the venue:no need to ring you can just enter in the building and come upstairs

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

After a successful kick-off event, we are pleased to announce the second in-person meetup of the MLOps community, which will take place on April 27, 2023 at 17h00 at the Moov AI office. This meetup promises to be exciting as we welcome two esteemed guest speakers who will share their expertise on topics that are relevant to all of us.

Our first guest speaker is Julien Vong, Senior Product Manager at Potloc, who will present on the topic "Convince Stakeholders Without a Data Background." Many of us have experienced the challenge of communicating the value of our work to stakeholders who lack a data background.

Our second guest speaker is Ezzat Demnati, a Data Scientist & Solution Architect at Microsoft. Ezzat will be presenting on the topic "Adopt MLOps Principles in Large-Scale Organizations." This presentation will be particularly relevant to those of us working in large organizations where scaling MLOps can be a complex and daunting task.

Join us at the Moov AI offices for networking, pizza, and interesting discussions. Find Moov AI at 4115, St-Laurent Boulevard, Suite 300, Montréal, Québec, H2W 1Y8. Enter the main building doors at street level. A team member will be waiting to greet you, and directional signage will be placed throughout.

We encourage all members of the MLOps community in Montreal to join us for this exciting event. We look forward to seeing you there!

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The agenda for this second meetup will be as follows:
17:00 - Doors open
17:15 - Introduction
17:30 - Presentation #1
--- "Convince Stakeholders Without a Data Background" by Julien Vong
18:00 - Coffee break and networking
18:15 - Presentation #2
--- "Adopt MLOps Principles in Large-Scale Organizations" by Ezzat Demnati
18:45 - Raffle and wrap-up
19:00 - Drinks and food kindly offered by Moov AI
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Convince stakeholders without a data background
Change can be difficult, especially when it comes to implementing new data science and MLOps initiatives. Whether you’re introducing new tools, processes, or workflows, you’re likely to encounter resistance and pushback from your stakeholders. This is especially true if they lack a background in data. Based on concrete examples from Julien’s experience and reference literature, he will share best practices on how to convince these stakeholders and ensure that change management leads to success.
About Julien:
Julien, Senior Product Manager at Potloc, has over 10 years of experience driving data science innovation. He began in business consulting before transitioning to product management, where he specializes in developing and implementing data science products to solve complex problems and drive business impacts. His unique skill set bridges the gap between data science, engineering, and business teams to create valuable products for both the business and customers.

Adopt MLOps principles in large-scale organizations
The number of Machine learning (ML) projects is growing rapidly, and organizations must innovate by integrating ML solutions to generate business value and remain competitive.
So, how can large-scale organizations deliver ML projects more efficiently?
Today, DevOps principles are widely adopted as they improve the software development lifecycle through test automation, continuous integration, and continuous deployment. In addition, organizations can reduce overall cost and time to market by adopting MLOps principles and choosing the right tools to deliver ML solutions.
This talk will introduce you to MLOps adoption in large organizations and their benefits for delivering Machine Learning projects.
About Ezzat:
Ezzat Demnati is a Data Scientist & Solution Architect as part of the National AI team at Microsoft Canada, with over 15 years of professional experience on data platforms applied to different industries such as financial services, insurance, and the public sector. He holds two master's degrees in computer science and data science and business analytics. Over the years, Ezzat played different roles as a business analyst, solution architect, and tech lead. His main areas of specialization include big data with Spark, ML engineering, ML Ops, and Applied Machine learning.