Toronto Machine Learning Series (TMLS)

Ebrahim & Jisheng - Automated Pipeline for Large-Scale Neural Network Training and Inference

Understanding Content Using Deep Learning For Natural Language Processing

State of the Art Deep Learning Based Object Detection in 2D

Mark McQuade and Tanya Vucetic - Automating Production Level Machine Learning Operations on AWS

Kenny Daniel - DevOps for ML and other Half-Truths: Processes and Tools for the ML Lifecycle

Building a MovieLens Recommender System

Vinnie Saini - RPA and AI disrupting the Financial Industry

Sheldon Fernandez - COVID-Net for COVID detection and risk stratification via chest radiography

Don Ward - Edge AI - The Next Frontier

TMLS2019 - Jaya Kawale

Srikar Kovvali - Workshop: Post-COVID Supply Chain MLOps Model Re-Training

TMLS2019 - Satya Krishna Gorti

Dmitry Baev - Deploying Distributed AI and Machine Learning in Financial Services

TMLS2020 Instructional Video

Tina White - Decentralized exposure alert protocols for protecting communities from COVID-19

TMLS2019 - Laila Paszti

Hamza Tahir - Why ML in production is STILL broken?

Stephen Mackinnon - PolypharmDB, Quickly Identifies Repurposed Drug Candidates for COVID-19

TMLS - 2019 Sheldon & Michael

Autonomous Vehicles-The Next Step Forward

Machine Learning Simplified from Ideation to Deployment in Minutes with Automated Machine

Lina Weichbrodt - How To Monitor Machine Learning Stacks

Alexander Wong - Workshop: How a human-machine collaboration approach to deep learning development

Dean Wampler - Ray and how it enables easier DevOps

Innodata Presents: Bogged Down by Annotation Why SMEs Should Do the Heavy Lifting

Women in AI: Transitioning Careers into AI, Challenges, Opportunities from a Female Perspective

Founder's Circle: Open Discussion with ML Entrepreneurs

Josh Poduska - Workshop: Turbo-Charging Data Science with AutoML

Deploying deep learning models to the edge

A Tale of A Thousand Pipelines

Moez Ali - Workshop: Machine Learning with PyCaret

Build, Train, and Deploy models with Amazon SageMaker

AI to AEYE: See the Value of AI as Investor & 5 Key Factors That Can Attract Investors to AI company

Sabina Stanescu - Your First ML Model In Production: Examples & Considerations

Lina Palianytsia - Metrics: Holistic Health Metrics of ML-Based Product

Human Interpretable Machine Learning for Smart Water Management

Saranyan - MLOps that works: How we built ML pipelines for deploying models for autonomous factories

Bahareh, Eric and Safura - Workshop: Trusted AI: a workshop on IBM Watson OpenScale

Brandy Freitas - Team Roles in a Machine Learning Project and Project Flow

Industry Survey Analysis: The industry Landscape of Natural Language Use Cases in 2020

Parnian Afshar - Capsule Network-based Framework for Identification of COVID cases from X-ray Images

Subhodeep Moitra - Deep Learning for Program Repair

Black-Box and Glass-Box Explanation in Machine Learning

Jan Zawadzki - The Do’s and Don’ts of Delivering AI Projects: A Practitioners Guide

Knowledge Graph Recommendation Systems for COVID 19

Managing Data Science in the Enterprise

Dr. Neeraj Kashyap - Machine Learning

How to automate machine learning with GitHub Actions

Xiaoming Zhang - Productionizing ML Models at Online Shopping at Loblaws

Dr. Benjamin Fine & Liam McCoy - HowsMyFlattening: Ontario's COVID-19 Data Analytics Collaborative

Docker Based Workflow for Deploying a Machine Learning Model