Save 25% on tickets to TMLS 2021

Updated: Oct 1

Ask AI supporters can use our promo code to save 25% on tickets to the 5th annual Toronto Machine Learning Summit.

The Toronto Machine Learning Summit will take place virtually between 10am on Monday, November 15, 2021 and 6PM on Thursday, November 18, 2021. This year's theme is "A virtual exploration of top use-cases, research, and business strategies"


Ask AI promo code


Enter this code when checking out to save 25%: ASKAI25


Book your spot


Visit the TMLS Eventbrite page to register for this year's event:



Featured talks at TMLS 2021


Here's a preview of a few of the many talks and working sessions lined up for this year's event:


Researchers Gone Wild

Adam Harvey, Independent Researcher - Exposing.ai

This talk will discuss the Exposing.ai research project, a multi-year investigation into the origins and endpoints of biometric image training datasets created from "media in the wild". Over the past years several prominent datasets, including MS-Celeb-1M, DukeMTMC, VGGFace2, and MegaFace have been retracted, heavily criticized, or mysteriously deprecated without explanation.


Trade-off between Optimality and Explainability

Nima Safaei, Sr. Data Scientist, Scotiabank

One of the top challenges in AI/ML is the black box models cannot be trusted in high-risk areas due to lack of explainability. Generally speaking, Explainability in ML is two folded, Casual Explainability (also known as interpretability) and Counterfactual Explainability. While the former addresses ‘why’, the latter addresses ‘how’ small and plausible perturbations of the input modify the output? The author’s focus is on Counterfactual Explainability from the optimization lens.


Unsolved Problems in Human-in-the-Loop Machine Learning

Robert Monarch, Author, Human-in-the-Loop Machine Learning

This talk will feature excerpts from my recently published book "Human-in-the-Loop Machine Learning: Active learning and annotation for human-centered AI". I'll cover some of the most exciting problems in Human-in-the-Loop Machine Learning and promising recent advances that address some of these problems.


How HelloFresh Leverages Feature Engineering and Modelling Techniques to Inform Menu Design

Delina Ivanova, Senior Manager, Data, Analytics & Insights, HelloFresh Canada

With advancements in technology, specifically around an organization's ability to make sense of data, consumers are growing more accustomed to personalized, on-demand solutions. In business like HelloFresh where the objective is to offer just that - solutions - product design and re-design is a continuous process.


Semantic Scholar, NLP, and the Fight against COVID-19

Oren Etzioni, Allen Institute for AI - CEO

This talk will discuss projects focused on advancing AI for the common good, including the dramatic creation of the COVID-19 Open Research Dataset (CORD-19) and the broad range of efforts, both inside and outside of the Semantic Scholar project, to garner insights into COVID-19 and its treatment based on this data. It will also highlight key advances in NLP that have enabled this work.


Featured workshops at TMLS 2021


Here's a preview of just a few of the many talks and working sessions lined up for this year's event:


NLP Without a Ready-made Labeled Dataset

Sowmya Vajjala, National Research Council, Canada - Researcher

NLP tutorials and workshops typically start with a labeled/annotated dataset, and discuss different ways of representing text/building models.


Algorithmic Bias in Human Resources, Responsible AI, and Business Strategy

Ushnish Sengupta, Ph.D. Candidate, University of Toronto

This session discuses Algorithmic Bias in Human Resources including AI and ML projects. It provides a convincing case that the bias and fairness issues that have been identified are grounded in business strategy decisions.


MLOps Without Much Ops

Jacopo Tagliabue, Coveo - Lead AI Scientist

It is indeed a wonderful time to build machine learning systems, as we don’t have much to do anymore! Thanks to a growing ecosystem of tools and shared best practices, even small teams can be incredibly productive at “reasonable scale”.


Towards Observability for Machine Learning Pipelines

Shreya Shankar - Ph.D. Student, UC Berkeley

Software organizations are increasingly incorporating machine learning (ML) into their product offerings, driving a need for new data management tools.


Get tickets


Visit the TMLS Eventbrite page to register for this year's event. Enter the the Ask AI when checking out to save 25%: ASKAI25

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