Toronto Machine Learning Meetup


May 30, 05:00 PM EDT
  • Toronto (Canada) AICamp
  • 197 RSVP
Description
Speaker

Welcome to our in-person machine learning meetup in Toronto. Join us for deep dive tech talks on AI/ML/Data, food/drink, networking with speakers & peers developers.

Agenda (EDT):
* 5:00pm~5:30pm: Checkin, food/drink and networking
* 5:30pm~7:30pm: Tech talks
* 7:30pm: Open discussion & Mixer

This meet-up is a unique opportunity to connect with fellow AI enthusiasts, industry practitioners, and researchers in a dynamic and interactive setting. Whether you are a seasoned AI professional or just curious about the latest advancements in AI, ML, NLP, LLMs and ChatGPT, this meet-up is for you! Join us for an insightful and thought-provoking discussion on the forefront of AI innovation.

Please note that a QR-Code will be emailed to all registered attendees a day before the event, which will serve as your entry pass for the meetup. We may not let you in the building without it.

Tech Talk 1: A practical guide to LLMs in Azure (Including Azure OpenAI)
Speaker: Hossein Sarshar, Senior ML Architect @Microsoft
Abstract: This session is aimed at explaining the practical aspects of implementing LLM models in Azure. A special emphasis will be placed on Azure OpenAI, detailing its features, use-cases, and the implementation details. The guide on how to operationalize open source LLM models on Azure will also be discussed. By the end of this talk, attendees will have an understanding of LLMs in Azure in particular Azure OpenAI.

Tech Talk 2: Embeddable Graph Database Management System
Speaker: Semih Salihoglu, Associate Professor @University of Waterloo
Abstract: In this talk, I will present the Kùzu graph database management system (GDBMS): an embeddable and feature-rich open-source DBMS that is optimized for ease of use, performance, and scalability. Datasets and workloads of popular applications that use GDBMSs require a set of storage and query processing features that relational DBMSs (RDBMSs) do not traditionally optimize for. These include optimizations for: (i) many-to-many (m-n) joins; (ii) cyclic joins; (iii) recursive joins; (iv) semi-structured data storage; and (v) support for universal resource identifiers.

Tech Talk 3: Explainability for boosting machines
Speaker: Ali Madani, Director of ML @Cyclica
Abstract: Boosting techniques and Python libraries such as XGBoost and LightGBM are among the top performers for majority of problems with tabular datasets. However, developing reliable machine learning models for production is not limited to optimizing for performance. Explainability has become an important topic in machine learning to unlock complex models and provide understandable information regarding the relationship between input features and output variables.
In this talk, we will review LightGBM, practice with it in Python and then try to explain the model using available python libraries for machine learning explainability.

Venue:
Microsoft Canada (CIBC Square), 81 Bay Street, Toronto ,Ontario ,M5J 2T3 Google Map

Lucky draw
We will raffle winners for prizes during the event. To enter the lucky draw, share the event on social media:

  • Prefer Twitter? Twitter the event with hashtag #aicamptoronto and tag @aicampai. For example:
    • #aicamptoronto Join the monthly AI meetup in Toronto by @aicampai to learn AI, ML, Data and Cloud technology with tech leads and industry experts. Free join in person: https://www.aicamp.ai/event/eventdetails/W2023053014
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    • Community on Slack
    - Event chat: chat and connect with speakers and attendees
    - Sharing blogs, events, job openings, projects collaborations
    Join Slack (search and join the #toronto channel)

    Hossein Sarshar (Microsoft),Ali Madani

    Hossein Sarshar
    Senior ML Architect at Microsoft and for the past five years he helped Microsoft enterprise customers solve their machine learning problems on Microsoft Azure. During his tenure, he led several ML projects from NLP to RecSys. One of his main activities has been around engineering aspects of ML projects including MLOps. He actively implemented and advised several initiatives in this regard which helped to significantly reduce the total cost of ownership of ML projects in complex settings. Prior to his career in ML, he was a software engineer for almost a decade.
    Ali Madani
    Director of Machine Learning at Cyclica, a Toronto based biotechnology company and guides scientists and engineers to further improve Cyclica’s deep learning and machine learning technologies for drug discovery. As a machine learning specialist, Ali has worked on a series of scientific articles in high impact scientific journals, patented technologies and international conferences covering such fields as transfer learning, graph neural networks and representation learning with the focus on healthcare applications. He was also Editor of special Topic Artificial Intelligence In Cancer Diagnosis and Therapy at MDPI that resulted in a book including a comprehensive list of papers on this topic. Ali is a believer in democratization of knowledge and has educated academic and industrial AI in Healthcare communities through his courses, webinars and public content.
    Semih Salihoğlu
    Associate Professor at University of Waterloo. His research focuses on developing systems for managing, querying, or doing analytics on graph-structured data.
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