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.
* 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.
Microsoft Canada (CIBC Square), 81 Bay Street, Toronto ,Ontario ,M5J 2T3 Google Map
We will raffle winners for prizes during the event. To enter the lucky draw, share the event on social media:
#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
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)