Toronto Machine Learning Series (TMLS) Courses

Leveraging ML Recommendation Models to Improve Customer Engagement

Explore personalization in retail using Amazon Personalize. Learn about ML-powered recommendation systems, real-time personalized experiences, and their impact on customer engagement.

• YouTube
• 1 hour 6 minutes
• Self-Paced
• Free Video

Adapting Continuous Integration and Continuous Delivery for Machine Learning

Explore MLOps practices for managing ML complexity, adapting CI/CD workflows using Git, GitHub Actions, and DVC to balance experimentation with stability in production.

• YouTube
• 34 minutes
• Self-Paced
• Free Video

Accelerating Public Health Research: Why Healthcare Needs MLOps

Explore MLOps applications in healthcare to accelerate public health research and improve patient outcomes through advanced machine learning techniques.

• YouTube
• 1 hour 31 minutes
• Self-Paced
• Free Video

Grassroots Responsible AI - Operationalizing AI Ethics

Operationalize responsible AI using ML observability techniques. Implement model transparency, analyze effects of changes, and use explainability to determine issues. Gain insights from thought leaders on ethical AI approaches.

• YouTube
• 1 hour 23 minutes
• Self-Paced
• Free Video

The Quest for the Final Quantum Computer

Explore quantum computing basics, cutting-edge algorithms, and future prospects in experimental quantum computers with insights from a postdoctoral researcher.

• YouTube
• 32 minutes
• Self-Paced
• Free Video

Towards Observability for Machine Learning Pipelines

Explore end-to-end observability for ML pipelines. Learn about a system that enhances visibility, executes tests, tracks data flow, and enables post-hoc analysis of pipeline health.

• YouTube
• 1 hour 22 minutes
• Self-Paced
• Free Video

Capsule Networks: An Introduction

Explore capsule networks and their potential to revolutionize computer vision, addressing limitations of convolutional neural networks in image recognition tasks.

• YouTube
• 29 minutes
• Self-Paced
• Free Video

Creating Harmony Between Machine Learning Engineers and Researchers

Discover strategies to bridge the gap between ML engineers and researchers, enhancing collaboration and efficiency in AI projects.

• YouTube
• 45 minutes
• Self-Paced
• Free Video

Feature Engineering and Modeling Techniques for Menu Design at HelloFresh

Discover how HelloFresh uses data-driven approaches to optimize menu design, predict recipe performance, and align product offerings with customer preferences and business strategies.

• YouTube
• 43 minutes
• Self-Paced
• Free Video

Training Custom Clinical NER Models with Spark NLP

Learn to train custom clinical Named Entity Recognition models using Spark NLP, exploring advanced techniques for healthcare data extraction and analysis.

• YouTube
• 1 hour 25 minutes
• Self-Paced
• Free Video

Natural Language Processing for Automated ESG Portfolio Construction

Explore NLP techniques for automated ESG portfolio construction, combining machine learning with portfolio optimization to incorporate ESG performance views from news data.

• YouTube
• 38 minutes
• Self-Paced
• Free Video

Develop and Deploy ML Projects with Metaflow and Seldon

Develop robust ML workflows and deploy models at scale using Metaflow and Seldon. Learn from core developers how to prototype, train, and deploy production-grade ML infrastructure across the full project lifecycle.

• YouTube
• 1 hour 54 minutes
• Self-Paced
• Free Video

Automating Production Level Machine Learning Operations on AWS

Explore ML Ops challenges and solutions using AWS services. Learn to bridge the gap between data science and deployment with a "model factory" architecture for effective CI/CD in machine learning.

• YouTube
• 1 hour 6 minutes
• Self-Paced
• Free Video

Literate Statistical Programming for Reproducible Data Science

Learn a reproducible, auditable workflow for data science projects using Literate Statistical Programming principles, tools, and techniques for improved experimentation and iteration.

• YouTube
• 46 minutes
• Self-Paced
• Free Video

How to Build a Real-Time ML Pipeline for Fraud Prediction

Build a real-time ML pipeline for fraud prediction, addressing challenges in online feature engineering and real-time data ingestion. Learn to generate ML features, use a feature store, and monitor for drift.

• YouTube
• 47 minutes
• Self-Paced
• Free Video