Reproducibility & Data Version Control
for LangChain & LLM/OpenAI Models

FREE Virtual Workshop
Nov. 29th,1PM EST

Register

Proudly Sponsored By

Presenter: Amit Kesarwani
Director of Solution Engineering
lakeFS by Treeverse

Get your ticket today

Toronto Machine Learning Summit

8th Annual Toronto Machine Learning Summit

Celebrating Canadian Applied AI Innovation

Buy Tickets

July 12th - 15th, 2024

Virtual Event & In-Person

Drop us a line: info@torontomachinelearning.com
Sponsorships: faraz@torontomachinelearning.com

Why Attend

A Unique Experience

For 8 years TMLS has hosted a unique blend of cutting-edge research, hands-on workshops, & vetted industry case studies reviewed by the Committee for your team’s expansion & growth.

We emphasize community, learning, and accessibility.

Join Today

Explore the Uncharted Frontiers of Generative AI

Big Ideas Showcase

See groundbreaking innovations and meet the innovators pushing technological boundaries in Gen-AI.

See More

Explore & Network

Explore real-world case studies and cut through the hype, gain valuable insights into the latest advancements, trends and advances around its deployment in production environments in this rapidly evolving field. Network with fellow practitioners and business leaders.
See More

Event Speakers

Jullian Yapeter

Jullian Yapeter

Machine Learning Scientist, Signal 1
Talk Title: AI for Hospitals at Scale

Estefania Barreto

Estefania Barreto

ML Engineer, Recursion Pharmaceuticals
Talk Title: Industrializing ML Workflows in Drug Discovery

Angela Xu

Angela Xu

Director, Risk Control and Fraud Analytics, CIBC
Talk Title: Revolutionizing Fraud Prevention: Harnessing AI and ML to Safeguard Banking from Fraud

Patricia Thaine

Patricia Thaine

Co-Founder & CEO, Private AI
Talk Title: Building Customer Trust in the Generative AI Era

Suhas Pai

CTO, Hudson Labs
Talk Title: Making RAG (Retrieval Augmented Generation) Work

Yannick Lallement

Chief AI Officer, Scotiabank
Talk Title: Gen AI in Banking: Lessons Learned

Everaldo Aguiar

Everaldo Aguiar

Senior Engineering Manager, PagerDuty
Panel: RAGs in Production: Delivering Impact Safely and Efficiently

Rohit Saha

Machine Learning Scientist, Georgian
Workshop: Leveraging Large Language Models to Build Enterprise AI

View All Speakers

See Full Agenda | Reserve your spot today!

Download Now

Sponsors & Partners

Platinum Sponsor
Sponsors
Exhibitors

Who Attends

Data Practitioners
0 %
Researchers/Academics
0 %
Business Leaders
0 %

2023 Event Demographics

Highly Qualified Practitioners*
0 %
Currently Working in Industry*
0 %
Attendees Looking for Solutions
0 %
Currently Hiring
0 %
Attendees Actively Job-Searching
0 .0%

2023 Technical Background

Expert
17.5%
Advanced
47.3%
Intermediate
21.1%
Beginner
5.6%

2023 Attendees & Thought Leadership

Speakers
0 +
Company Sponsors
0 +

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 30+ Top Al Companies.

Why

TMLS

Attend

TMLS is a community response addressing the need to unite academic research, industry opportunities and business strategy in an environment that is safe, welcoming and constructive for those working in the fields of ML/AI.

See our team and learn more about the Toronto Machine Learning Society here.

Interested in Partnerships?

Email for Brochure: faraz@torontomachinelearning.com

Tickets

This event has ended
This event is no longer available.
Attend
TMLS
2023
Talk Title: AI for Hospitals at Scale

Presenter:
Jullian Yapeter, Machine Learning Scientist, Signal 1

About the Speaker:
Jullian is a Machine Learning Scientist at Signal 1. His focus is at the intersection of model development and ML infrastructure / MLOps. He is an engineer with a BASc. in Mechatronics Engineering from the University of Waterloo, and a M.S. in Computer Science from the University of Southern California. He was a research assistant at the CLVR Lab at USC, working on large-scale Offline RL under Prof. Joseph Lim. Jullian has industry experience working on AI / Computer Vision systems at Disney Imagineering, IBM, and a few different start-ups. Overall, he’s passionate about improving people’s lives through technology.

Talk Track: Research or Advanced Technical

Talk Technical Level:  5/7

Talk Abstract:
An exploration into the technical processes employed at Signal 1 that enables the training and deployment of machine learning models across various hospital settings, including zero-shot learning applications in patient deterioration prediction that generalizes even to unseen hospitals.

This talk will also cover the specifics of our microservice architecture which underpins our system’s capability to consistently deliver timely and effective inference results, enabling scalable, data-driven decisions in patient care.

Attendees will gain insights into the practical challenges and solutions encountered in developing AI applications that can seamlessly integrate into and impact real-world clinical settings.

Whether you’re interested in the nuances of model development, deployment, or the practical implications of AI in healthcare, this session will offer valuable technical knowledge and perspectives.

We invite you to join this technical discourse at the intersection of AI and healthcare, contributing to a dialogue that’s shaping the future of AI applications in medical settings.

What You’ll Learn:
– An overview of the practical challenges in deploying ML in hospitals, such as generalization and scalability
– How we at Signal 1 tackle some of these challenges
– Discussions about some of the problems we’re still working on

Talk Title: Industrializing ML Workflows in Drug Discovery

Presenter:
Estefania Barreto, ML Engineer, Recursion Pharmaceuticals

About the Speaker:
Estefania Barreto-Ojeda is an ML Engineer at Recursion, where she builds and automates machine learning pipelines for drug discovery. A physicist by training, she has a PhD in Biophysical Chemistry from the University of Calgary where she participated in Google Summer of Code as an open source software developer . She has given talks at several major data conferences, including PyData. Estefania is a full time automation fan, an occasional open-source contributor, and a seasonal bicycle lover.

Talk Track: Research or Advanced Technical

Talk Technical Level:  5/7

Talk Abstract:
Recursion is committed to industrialize drug discovery by addressing the complexities of Machine Learning (ML) workflows head-on. A critical step in the drug discovery process is predicting compounds’ properties such as Absorption, Distribution, Metabolism, and Excretion (ADME), Potency and Toxicity among others, which allows the evaluation of a drug candidate for safety and efficacy, crucial for regulatory approval. In order to leverage its large volume of diverse and regularly updated chemical assays datasets, Recursion has engineered standardized and automated solutions to train and deploy predictive models on a weekly basis, thus accelerating the drug discovery process in early stages. In this talk, we will offer a comprehensive overview of our industrialized workflows to develop and deploy ML compound property predictors. Insights into Recusion’s strategy for data management, model training and deployment using both cloud and supercomputing resources will be shared.

What You’ll Learn:
During this presentation, attendees will gain understanding of our structured approach for creating and implementing machine learning models to predict compound properties in an industrial setting. We will explore Recusion’s approach to managing data, training models, and deploying them utilizing a combination of cloud services and supercomputing resources

Talk Title: Revolutionizing Fraud Prevention: Harnessing AI and ML to Safeguard Banking from Fraud

Presenters:
Angela Xu, Director, Risk Control and Fraud Analytics, CIBC | Kemi Borisade, Senior Fraud Data Analyst, CIBC

About the Speakers:
Angela Xu brings over 15 years of strategic data analytics experience in premier financial institutions to the Toronto Machine Learning Summit Conference. As a seasoned technical expert and strategic thinker, Angela has demonstrated success in developing and implementing innovative strategies. With a Master’s degree in Statistics from the Georgia Institute of Technology in Atlanta, USA, and another Master’s degree in Computer Science from China, Angela possesses a diverse skill set that she leverages to drive initiatives to tangible results.

Currently leading the Risk Control & Fraud Analytics team at CIBC, Angela focuses on regulatory breach reporting and fraud strategies for secured and unsecured lending products such as mortgages, loans, and lines of credit. Her leadership is characterized by a commitment to generating innovative ideas, influencing stakeholders, and delivering real value to both her organization and its clients.

Passionate about leveraging cutting-edge technologies to solve complex problems, Angela is dedicated to applying the latest advancements in machine learning and data analytics to add value to her company and enhance the experiences of its clients.

Talk Track: Business Strategy or Ethics

Talk Technical Level:  3/7

Talk Abstract:
In 2023, the Canadian Anti-Fraud Centre reported staggering losses of over CAD $550 million due to fraudulent activities, underscoring the urgent need for advanced security measures. At CIBC, we confront the dynamic challenges of this evolving landscape head-on by embracing cutting-edge tools, technologies, and methodologies.
Our journey is marked by formidable obstacles, including the limitations of rule-based fraud strategies, the delicate balance between sales and risk mitigation, inadequate tools for documentation validation, and the pressing demand for rapid fraud assessment. To address these challenges, our team embarked on a transformative path, leveraging next-generation self-learning Machine Learning models supplemented with custom thresholds. This approach enhances fraud detection capabilities, minimizes false positives, optimizes sales strategies, and fortifies client protection.
Furthermore, through strategic partnerships, we’ve embraced solutions such as Optical Character Recognition (OCR) to streamline documentation validation processes. Exploring the integration of graph databases, Natural Language Processing (NLP), and foundational models, we aim to unlock new frontiers in fraud prevention.
The culmination of our efforts heralds a new era in security, where the synergy of advanced AI and ML technologies promises unparalleled efficiency and efficacy in combating fraud. Join us as we unveil the future of fraud prevention in Canadian banking.

Additional notes

Dear Organizers and Evaluators,

I hope this letter finds you well. Over the years, I have had the privilege of attending the Toronto Machine Learning Summit Conference, and each time, I have found immense value in the exchange of ideas and the learning opportunities it provides. It has been a platform where I have personally benefited and grown in my understanding of machine learning and its applications.

This year, I am excited to contribute to the conference by sharing insights into the latest trends and technologies in fraud detection within financial institutions. My presentation aims to raise awareness among the audience about the critical importance of fraud prevention measures for both institutions and individuals alike. By exploring the advancements in machine learning and artificial intelligence, I hope to inspire discussions on innovative strategies to safeguard company assets and personal finances.

Fraud prevention is a pressing concern in today’s interconnected world, and I believe that through collaboration and knowledge-sharing at events like the Toronto Machine Learning Summit Conference, we can collectively work towards more effective solutions. I am eager to engage with fellow attendees, exchange perspectives, and explore new avenues for leveraging technology in the fight against fraud.

What You’ll Learn:
After attending this presentation, you will gain a comprehensive understanding of the prevailing fraud challenges within the financial industry. You will also acquire foundational knowledge of next-generation near real-time self-learning Machine Learning models, along with insights into their fundamental concepts. Additionally, you’ll explore advanced cutting-edge technologies utilized in fraud detection, equipping you with valuable insights into the evolving landscape of financial security.

Talk Title: Building Customer Trust in the Generative AI Era

Presenter:
Patricia Thaine, Co-Founder & CEO, Private AI

About the Speaker:
Patricia Thaine is the Co-Founder & CEO of Private AI, a Microsoft-backed startup who raised their Series A led by the BDC in November 2022. Private AI was named a 2023 Technology Pioneer by the World Economic Forum and a Gartner Cool Vendor. She is also a Computer Science PhD Candidate at the University of Toronto (on leave) and a Vector Institute alumna. Her R&D work is focused on privacy-preserving natural language processing, with a focus on applied cryptography and re-identification risk. She also does research on computational methods for lost language decipherment. Patricia is a recipient of the NSERC Postgraduate Scholarship, the RBC Graduate Fellowship, the Beatrice “Trixie” Worsley Graduate Scholarship in Computer Science, and the Ontario Graduate Scholarship. She is the co-inventor of one U.S. patent and has ten years of research and software development experience, including at the McGill Language Development Lab, the University of Toronto’s Computational Linguistics Lab, the University of Toronto’s Department of Linguistics, and the Public Health Agency of Canada.

Talk Track: Business Strategy or Ethics

Talk Technical Level:  3/7

Talk Abstract:
As one of the hottest technological advancements of the moment, ChatGPT has gained both attention and criticism for its privacy implications. This session explores the ethical challenges posed by generative AI and outlines strategies to establish trust in this evolving landscape. Covering topics such as privacy, bias, and GDPR compliance, attendees will gain practical insights to navigate the ethical complexities of the generative AI era and contribute to building a trustworthy AI future.

What You’ll Learn:
– Understand the ethical challenges of generative AI, including privacy concerns, bias, and GDPR compliance.
– Learn strategies for identifying and mitigating bias in AI-generated content, promoting fairness and inclusivity.
– Gain insights into safeguarding user privacy through data minimization, anonymization, and more.

Talk Title: Making RAG (Retrieval Augmented Generation) Work

Presenter:
Suhas Pai, CTO, Hudson Labs

About the Speaker:
Suhas Pai is a NLP researcher and co-founder/CTO at Hudson Labs a Toronto based startup. At Hudson Labs, he works on text ranking, representation learning, and productionizing LLMs. He is also currently writing a book on Designing Large Language Model Applications with O’Reilly Media. Suhas has been active in the ML community, being the Chair of the TMLS (Toronto Machine Learning Summit) conference since 2021 and also NLP lead at Aggregate Intellect (AISC). He was also co-lead of the Privacy working group at Big Science, as part of the BLOOM open-source LLM project.

Talk Track: Applied Case Studies

Talk Technical Level:  6/7

Talk Abstract:
The RAG (Retrieval Augmented Generation) paradigm drives a large proportion of LLM-based applications. However, getting RAG to work beyond prototypes is a challenging ordeal. In this talk, we will go through some of the common pitfalls encountered when implementing RAG along with techniques to alleviate them. We will showcase how robustness can be built into the design of the RAG pipeline and how to balance them against factors like latency and cost.

What You’ll Learn:
What can go wrong with RAG?

Techniques to alleviate RAG shortcomings – specifically, tightly coupled models, layout and context-aware fine-tuned embeddings, retrieval text refinement, query expansion, and interleaved retrieval.

Talk Title: Gen AI in Banking: Lessons Learned

Presenter:
Yannick Lallement, Chief AI Officer, Scotiabank

About the Speaker:
Yannick Lallement is the VP & Chief AI Officer at Scotiabank, where is works on developing the use of AI/ML technologies throughout the Bank. Yannick holds a PhD in artificial intelligence from the French National Institute of Computer Science. Prior to joining Scotiabank, Yannick worked on a series of AI/ML projects for different public and private organizations.

Talk Track: Applied Case Studies

Talk Technical Level:  2/7

Talk Abstract:
I will present Scotiabank’s Gen AI journey so far, from collecting ideas across the bank in an inventory all the way to our first use cases in production, and share what we learned along the way on how Gen AI applies to the industry (examples will be about banking, but lessons will be applicable across).

What You’ll Learn:
How Gen AI can effectively be useful, how to find the right use cases, how to deploy it at scale.

Talk Title: RAGs in Production: Delivering Impact Safely and Efficiently

Presenters:
Everaldo Aguiar, Senior Engineering Manager, PagerDuty | Wendy Foster, Data Products Leader, Shopify

About the Speakers:
Everaldo started his Data Science journey as a Data Science for Social Good Fellow at the Center for Data Science and Public Policy at UChicago. Today he is a Senior Engineering Manager at PagerDuty where he leads both the Data Science and Data Engineering teams, and a faculty member at the Khoury College at Northeastern University. Prior to that he was a Data Science Lead at Shopify’s Growth organization. Everaldo is originally from Brazil and Seattle has been home to him for 6 years.

Wendy with over 10 years of experience leading data organizations at scale, Wendy Foster divides her time between data start-up advising and applied data science education; supporting the next wave of data leaders and innovation in this rapidly evolving space.

Talk Track: Panel Discussion

Talk Technical Level:  4/7

Talk Abstract:
“Urgent” and “unplanned” are among the least favorite words in any productive team’s dictionaries. Unexpected issues disrupt roadmaps, delay important work, lead to burnout, and hurt customer trust.

Here at PagerDuty we’ve been leveraging AI to help our customers experience fewer incidents and resolve the ones they do have faster. This often involves giving them streamlined access to information they need about our product, their individual setups, and an efficient way for them to get answers to complex answers on the fly.

As technologies evolved and we rolled out our generative AI infrastructure, RAGs became an excellent candidate for those use-cases. They allow for an easy-to-automate process of building “”knowledge bases”” and using those to power powerful chat-like applications, but productionalizing them in a safe manner is often more challenging than building these RAG systems themselves.

In this panel we’ll discuss some of these challenges, how we’ve been tackling them, as well as existing areas of open research we’re excited to pursue in the coming months.

What You’ll Learn:
Attendees will learn how to tackle some common (and uncommon) challenges that come with bundling RAG models into their own products. We’ll cover a few corner cases that were completely unexpected as well as automation processes that we designed to ensure that complex parts of our systems could be maintained with minimal engineering effort.

Workshop: Leveraging Large Language Models to Build Enterprise AI

Presenters:
Rohit Saha, Machine Learning Scientist, Georgian | Kyryl Truskovskyi, Machine Learning Scientist, Georgian

About the Speakers:
Rohit is a Machine Learning Scientist on Georgian’s R&D team, where he works with portfolio companies to accelerate their AI roadmap. This includes scoping research problems to building ML models to moving them into production. He has over 5 years of experience developing ML models across Vision, Language and Speech modalities. His latest project entails figuring out how businesses can leverage Large Language Models (LLMs) to address their needs. He holds a Master’s degree in Applied Computing from the University of Toronto, and has spent 2 years at MIT and Brown where he worked at the intersection of Computer Vision and domain adaptation.

Talk Track: Workshop

Talk Technical Level:  3/7

Talk Abstract:
Generative AI is poised to disrupt multiple industries as enterprises rush to incorporate AI in their product offerings. The primary driver of this technology has been the ever-increasing sophistication of Large Language Models (LLMs) and their capabilities. In the first innings of Generative AI, a handful of third-party vendors have led the development of foundational LLMs and their adoption by enterprises. However, development of open-source LLMs have made massive strides lately, to the point where they compete or even outperform their closed-source counterparts. This competition presents an unique opportunity to enterprises who are still navigating the trenches of Generative AI and how best to utilize LLMs to build enduring products. This workshop (i) showcases how open-source LLMs fare when compared to closed-source LLMs, (ii) provides an evaluation framework that enterprises can leverage to compare and contrast different LLMs, and (iii) introduces a toolkit to enable easy fine-tuning of LLMs followed by unit-testing (https://github.com/georgian-io/LLM-Finetuning-Toolkit)

What You’ll Learn:
By the end of this workshop, learn how to create instruction-based datasets, fine-tune open-source LLMs via ablation studies and hyperparameter optimization, and unit-test fine-tuned LLMs.

Prerequisite Knowledge Python + Familiarity with concepts such as prompt designing and LLMs

Sign Up for TMLS 2023 News Updates

Who Attends

Attendees
0 +
Data Practitioners
0 %
Researchers/Academics
0 %
Business Leaders
0 %

2023 Event Demographics

Highly Qualified Practitioners*
0 %
Currently Working in Industry*
0 %
Attendees Looking for Solutions
0 %
Currently Hiring
0 %
Attendees Actively Job-Searching
0 .0%

2023 Technical Background

Expert
17.5%
Advanced
47.3%
Intermediate
21.1%
Beginner
5.6%

2023 Attendees & Thought Leadership

Attendees
0 +
Speakers
0 +
Company Sponsors
0 +

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 30+ Top Al Companies.

Ignite what is an Ignite Talk?

Ignite is an innovative and fast-paced style used to deliver a concise presentation.

During an Ignite Talk, presenters discuss their research using 20 image-centric slides which automatically advance every 15 seconds.

The result is a fun and engaging five-minute presentation.

You can see all our speakers and full agenda here

Get our official conference app
For Blackberry or Windows Phone, Click here
For feature details, visit Whova