Toronto Machine Learning Series (TMLS)

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

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

Maximo & Javier - Smart Data Products: From prototype to production