We’ve planned 4 tracks to help tackle different perspectives, from speakers around the world;
Designed for anyone working with ML/AI
Sessions Includes:
Workshops of various Technical levels 1-7
Hands-on sessions & walk-throughs
Business and Strategy (non-technical) leadership sessions
“ML practices made accessible to companies of all sizes. This is an unique gathering!”
Business Leaders: C-Level Executives, Project Managers, and Product Owners will get to explore best practices, methodologies, principles, and practices for achieving ROI.
Engineers, Researchers, Data Practitioners: Will get a better understanding of the challenges, solutions, and ideas being offered via breakouts & workshops on Natural Language Processing, Neural Nets, Reinforcement Learning, Generative Adversarial Networks (GANs), Evolution Strategies, AutoML, and more.
Job Seekers: Will have the opportunity to network virtually and meet over 60 Top Al Start-ups and companies during the EXPO & Career Fair.
Business Leaders Data
Chief Client Officer
Chief Digital Officer
Chief Executive Officer
Chief Experience Officer
Chief Operating Officer
Chief Scientific Officer
Chief Technology Officer
Director of Innovation
Director of Talent
Data Practitioners
AI Developer
AI Lead
AI Project Lead
AI Solution Architect
Big Data Engineer
Biomed Engineer
Chief Data Scientist
Chief Scientist
Computational Linguist
Researchers / Students
AI Researcher
Applied Research Scientist
Assistant Professor
Data Science fellow
Director Meteorological R&D
Graduate Research Assistant
Informatics Research Associate
ML Masters Student
Machine Learning Researcher
Allstate Insurance Canada
Apotex
AT&T
Aviva
Bell Canada
Borealis AI
CAMH
Canada Pension Plan Investment Board
Canadian Red Cross
GoDaddy
Home Depot
Huawei
IBM
Indigo
Institute for Quantum Computing
Intact Financial
Integrate AI
Interac
Ontario Institute for Cancer Research (OICR)
Ontario Investment Office
Ontario Teachers Pension
Pelmorex
Postmedia Network
President’s Choice Financial
Princess Margaret Cancer Research Center
Rangle.io
PwC
No, this is actually for all those working with and interested in machine learning. We highlight top thought leaders globally, and our group consists on people from around the world.
Laptop or personal computer, and a strong, reliable wifi connection. Google Chrome is recommended to run the Virtual Conference platform.
All sessions will be recorded during the event (provided speaker permissions) and will be made available to attendees approximately 2-4 weeks after the event and be available for 12 months after release.
There is not. Everyone is welcome.
Tickets are refundable up to 30 days before the event.
The event will have three tracks: One for Business, one for Advanced Practitioners/Researchers and one for applied use-cases (Focusing on various Industries). Business Executives, PhD researchers, Engineers and Practitioners ranging from Beginner to Advanced. See Attendee Demographics and a list of the Attendee Titles from our past event here.
Yes, there will be spaces for company displays. You can inquire at faraz@torontomachinelearning.com.
Unity Health Toronto – VP: Data Science and Advanced Analytics; Director: Temerty Centre for Artificial Intelligence Research and Education in Medicine of the University of Toronto; Professor – University of Toronto
Dr. Mamdani is Vice President of Data Science and Advanced Analytics at Unity Health Toronto and Director of the University of Toronto Temerty Faculty of Medicine Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM). Dr. Mamdani’s team bridges advanced analytics including machine learning with clinical and management decision making to improve patient outcomes and hospital efficiency. Dr. Mamdani is also Professor in the Department of Medicine of the Temerty Faculty of Medicine, the Leslie Dan Faculty of Pharmacy, and the Institute of Health Policy, Management and Evaluation of the Dalla Lana Faculty of Public Health. He is also adjunct Senior Scientist at the Institute for Clinical Evaluative Sciences (ICES) and a Faculty Affiliate of the Vector Institute. In 2010, Dr. Mamdani was named among Canada’s Top 40 under 40. He has published over 500 studies in peer-reviewed medical journals. Dr. Mamdani obtained a Doctor of Pharmacy degree (PharmD) from the University of Michigan (Ann Arbor) and completed a fellowship in pharmacoeconomics and outcomes research at the Detroit Medical Center. During his fellowship, Dr. Mamdani obtained a Master of Arts degree in Economics from Wayne State University in Detroit, Michigan with a concentration in econometric theory. He then completed a Master of Public Health degree from Harvard University in 1998 with a concentration in quantitative methods.
Talk: Saving Lives with ML: Applications and Learnings
Abstract: Machine learning (ML) has transformed numerous industries but its application in healthcare has been limited. ML applications are expected to permeate healthcare in the near future with a recent explosion in academic and commercial activity. The application of ML in healthcare, however, is complicated by a variety of factors including the significant variability in needs, healthcare settings and patients served in these settings, workflows, and available resources. This talk will present a case study of Unity Health Toronto and its journey in developing and deploying numerous ML solutions into clinical practice, including bridging public and private sector partnerships to spread innovations internationally. The talk will also present a novel Canadian academic centre dedicated to artificial intelligence (AI) in medicine – the Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM) at the University of Toronto.
What You’ll Learn: The successful application of ML in healthcare is multifaceted and highly dependent on end-user engagement.
Innovative public-private partnerships are needed to spread ML applications globally.
Multidisciplinary, collaborative efforts will fuel innovations in the development and application of ML in healthcare.
Track: Case Study
Technical Level: 3
Founder, Arima
Winston is the founder of Arima, a Canadian based startup that provides consumer data to its users. Our flagship product, the Synthetic Society, is a privacy-by-design, individual level database that mirrors the real society. Built using trusted sources like census, market research, mobility and purchase patterns, it contains 10k+ attributes across North America and enables advanced modelling at the most granular level.
Prior to founding Arima, Winston was the Director of Data Science at PwC and Omnicom. Winston is also a part-time faculty member at Northeastern University Toronto and sits on the advisory board of the Master of Analytics program.
Talk: The Application of Mobile Location Data for Vending Machine Site Selection and Revenue Optimization.
Abstract: In this presentation, we present an innovative approach to utilizing mobility data to optimize the placement of vending machines in Canada. Coca-Cola has more than 10k vending machines in various locations and their ROI heavily depends on the amount of foot traffic next to them as well as who those people are. For this use case, we’ll be concentrating on using the super detailed mobility data to understand the difference between our best machines and worst at scale, and optimizing their location based on the mobility data to increase the ROI. In addition to the practical and business application, we’ll also be able to share the algorithms used and the tech stack with the audience.
What You’ll Learn: Mobility data as an alternative data source for consumer related analytics and its recency and granularity and really drive measurable business outcomes.
Track: Case Study
Technical Level: 4
Director of Advanced Analytics, Coca Cola
Nikita has over 10 years of experience in the Retail and Consumer Packaged Goods industries, working for companies like Loblaw and Sears. He is also an alumnus of the Master of Management Analytics program from Queen’s University, and holds a Bachelor of Finance & Economics degree from University of Toronto
Talk: The Application of Mobile Location Data for Vending Machine Site Selection and Revenue Optimization.
Abstract: In this presentation, we present an innovative approach to utilizing mobility data to optimize the placement of vending machines in Canada. Coca-Cola has more than 10k vending machines in various locations and their ROI heavily depends on the amount of foot traffic next to them as well as who those people are. For this use case, we’ll be concentrating on using the super detailed mobility data to understand the difference between our best machines and worst at scale, and optimizing their location based on the mobility data to increase the ROI. In addition to the practical and business application, we’ll also be able to share the algorithms used and the tech stack with the audience.
What You’ll Learn: Mobility data as an alternative data source for consumer related analytics and its recency and granularity and really drive measurable business outcomes.
Track: Case Study
Technical Level: 4
CEO, Claypot AI
Chip Huyen is a co-founder of Claypot AI, a platform for real-time machine learning. Previously, she was with Snorkel AI and NVIDIA. She teaches CS 329S: Machine Learning Systems Design at Stanford. She’s the author of Designing Machine Learning Systems, an Amazon bestseller in AI. She has also written four bestselling Vietnamese books.
Talk: Real-time Machine Learning: Architecture and Challenges
Abstract: Fresh data beats stale data for machine learning applications. This talk discusses the value of fresh data as well as different types of architecture and challenges of online prediction.
What You’ll Learn: Fresh data beats stale data for machine learning applications
Track: Technical
Technical Level: 5
Senior Data Scientist Specialist Solution Architect, RedHat Canada
Arthur is a senior data scientist specialist solution architect at RedHat Canada where with the help of open source software is helping organizations develop intelligent application ecosystems and bring them into production using MLOps best practices.
He is also pursuing his Ph.D. degree in Computer Science at Concordia University, Montreal, Canada, and he is a research assistant in the Software Perfomance Analysis and Reliability (SPEAR) Lab.
His research interests are related to AIOps with focus on performance and scalability optimization.
Workshop: Open Source Intelligent Application Delivery on Kubernetes
Abstract: The recent rise in popularity of containerized workloads demanded better ways to orchestrate and manage these workloads hence the creation of the Kubernetes platform.
When it comes to running intelligent application workloads which contain built-in AI/ML software components, the requirement of a Kubernetes platform as a service extends beyond agility, portability, flexibility and scalability as it is required to also answer to the datascientist’s dilemma: getting started and getting into production.
However, as the ML code is only a small part of the entire intelligent application ecosystem, with this workshop we present a showcase for using a Kubernetes platform and a blueprint architecture that proposes an answer to many challenges related to the development, deployment and management of distributed applications.
The user stories we shall focus on in this workshop concerning the developer, data scientist and operations engineer personas are:
– As a data scientist, I want to develop ML models using Jupyter Hub (lab/notebooks) as my preferred research environment.
– As a data scientist, I want my model to be deployed quickly so that it may be used by other applications.
– As a (fullstack) developer, I want to have quick access to resources that support the business logic of my applications, including databases, storage, messaging.
– As a (fullstack) developer, I want an automated build process to support new releases/code updates as soon as they are available in a git repository.
– As an operations engineer, I want an integrated monitoring dashboard to new applications available on the (production) infrastructure.
What You’ll Learn: Open source container platforms are a great option to integrate Machine Learning with any application or service by boosting productivity while maintaining a high level of security.
Technical Level: 4
Workshop: Beyond the Basics: Data Visualization in Python
Abstract: The human brain excels at finding patterns in visual representations, which is why data visualizations are essential to any analysis. Done right, they bridge the gap between those analyzing the data and those consuming the analysis. However, learning to create impactful, aesthetically-pleasing visualizations can often be challenging. This session will equip you with the skills to make customized visualizations for your data using Python.
While there are many plotting libraries to choose from, the prolific Matplotlib library is always a great place to start. Since various Python data science libraries utilize Matplotlib under the hood, familiarity with Matplotlib itself gives you the flexibility to fine tune the resulting visualizations (e.g., add annotations, animate, etc.). This session will also introduce interactive visualizations using HoloViz, which provides a higher-level plotting API capable of using Matplotlib and Bokeh (a Python library for generating interactive, JavaScript-powered visualizations) under the hood.
What You’ll Learn: Data visualization is essential for anyone working with data, but sometimes it can be difficult to create impactful visualizations in Python. In this workshop, we will move beyond the plotting basics and explore how to make compelling static, animated, and interactive visualizations.
Technical Level: 4
Business Leaders Data
Chief Client Officer
Chief Digital Officer
Chief Executive Officer
Chief Experience Officer
Chief Operating Officer
Chief Scientific Officer
Chief Technology Officer
Director of Innovation
Director of Talent
Director, Applied Analytics & Innovation
Investor
Enterprise Data Innovation
Executive Director, Enterprise Applications
Founder
Global Manager, Marketing
Legal Advisor
MD, Innovation and Emerging Tech
Portfolio Manager
President
Product Manager
Project Manager
Recruiter
Scrum Master
Senior Product Manager
Senior Associate
Strategy & Business Development
SVP Product
SVP Professional Services
Trader
VP, Digital Data and Analytics
VP, Marketing and Communications
VP Growth
VP Strategy & Operations Business Intelligence
VP, Artificial Intelligence
Data Practitioners
AI Developer
AI Lead
AI Project Lead
AI Solution Architect
Big Data Engineer
Biomed Engineer
Chief Data Scientist
Chief Scientist
Computational Linguist
Data Science Lead
Data Science Tooling Lead
Data Warehouse Lead
Director of Data
Director of Engineering
Distinguished Data Scientist
Full Stack Developer
Lead Firmware & Systems Engineer
Machine Learning Developer
Machine Learning Engineer
Machine Learning Engineer (NLP)
Machine Learning Researcher Machine
Learning Specialist
Manager, Application Development
Manager, Mobile Architecture
Platform Architect
Principal Consultant
Principal Data Scientist
Principal Software Engineer
Principle Architect
Senior Computer Vision Engineer
Senior Data Analyst
Senior Data Manager
Senior Data Scientist
Software Engineer
Sr. Cyber Security Advisor
Sr. Dir Engineering
Researchers / Students
AI Researcher
Applied Research Scientist
Assistant Professor
Data Science fellow
Director Meteorological R&D
Graduate Research Assistant
Informatics Research Associate
ML Masters Student
Machine Learning Researcher
MASc Student Researcher
Masters of Management in Artificial Intelligence
PhD Student, Researcher
Portfolio Manager/Student
Post-graduate Researcher
Postdoctoral Fellow
Postdoctoral Researcher
Principal Researcher
Professor
Quantum Deep Learning Researcher
Research Data Analyst
Research Engineer
Research Scientist
Researcher and Software Developer
Scientific Adviser
Senior Analyst – Research and Development
Senior Research Associate
Senior Research Economist
Senior Research Scientist
Software Research Developer, Algorithms
Sr. Research Methods Specialist
Student, Machine Learning
and more!
Allstate Insurance Canada
Apotex
AT&T
Aviva
Bell Canada
Borealis AI
CAMH
Canada Pension Plan Investment Board
Canadian Red Cross
Canadian Tire
Centre for Addiction and Mental
Health (CAMH)Crater Labs
CIFAR
Coca-Cola
CPP Investment Board
Deloitte
Element AI
Epson
Ernst & Young LLP
Exiger
Finastra
Flipp Inc
General Motors Canada
Georgian Partners
Google
GoDaddy
Home Depot
Huawei
IBM
Indigo
Institute for Quantum Computing
Intact Financial
Integrate AI
Interac
Jam3
John Hancock / Manulife
Johnson & Johnson
KDnuggets
KPMG
Loyalty One
Loblaw Companies Limited
McDonald’s Canada
Maple Leaf Sports & Entertainment
Mastercard Canada
McKinsey
Metrolinx
Microsoft
ModiFace Inc
MoneyKey
Nascent
Nestle
Ontario Genomics
Ontario Institute for Cancer Research (OICR)
Ontario Investment Office
Ontario Teachers Pension
Pelmorex
Postmedia Network
President’s Choice Financial
Princess Margaret Cancer Research Center
Rangle.io
PwC
SAS
Samsung Canada
Shopify
SickKids
StackAdapt
Statflo
Sun Life Financial
Symcor
Tableau Canada
Tangerine
TD Bank
TELUS
The Ontario Brain Institute
Thomson Reuters
TIBCO
TMX Group Limited
TSX
Uber
Vector Institute
VoiceX Labs
Wattpad
Walmart
and more!
