Toronto Machine Learning Series (TMLS) Courses

MLOps and Automation Workshop - Bringing Machine Learning to Production in a Few Easy Steps

Learn to deploy ML/AI to production faster using MLOps technologies. Explore automation techniques and best practices for efficient machine learning operations.

• YouTube
• 2 hours 1 minute
• On-Demand
• Free Video

How F.A.T. is Your ML Model Quality in the Era of Software

Explore F.A.T. (Fairness, Accountability, Transparency) properties for assessing ML model quality. Learn evaluation approaches, governance benchmarks, and bias mitigation techniques through case studies.

• YouTube
• 45 minutes
• On-Demand
• Free Video

Abstraction and Analogy in Natural and Artificial Intelligence

Explore the role of analogy-making in conceptual abstraction and understanding, and its importance for developing AI systems with humanlike intelligence.

• YouTube
• 50 minutes
• On-Demand
• Free Video

Put Deep Learning to Work - Accelerate Deep Learning through AWS SageMaker and ML Services

Accelerate deep learning with AWS SageMaker and ML services. Master distributed multiGPU BERT fine-tuning and GPU hosting for efficient model development and deployment.

• YouTube
• 2 hours 35 minutes
• On-Demand
• Free Video

Utterance Generator Fast Tracks Chatbot Training

Explore an utterance generator tool for efficient chatbot training, enhancing natural language understanding and streamlining development processes.

• YouTube
• 28 minutes
• On-Demand
• Free Video

Using Model Serving in Streaming Applications

Explore model serving approaches in streaming applications using Cloudflow framework. Learn to develop, orchestrate, and operate distributed streaming applications on Kubernetes efficiently.

• YouTube
• 48 minutes
• On-Demand
• Free Video

Fighting Pipeline Debt With Great Expectations

Learn to combat pipeline debt using Great Expectations, an open-source library for data validation, documentation, and profiling in complex data systems.

• YouTube
• 39 minutes
• On-Demand
• Free Video

Building an AI Capability in the United States Army

Explore the development of AI capabilities in the U.S. Army with insights from Chief Data Scientist LTC Isaac J Faber PhD.

• YouTube
• 48 minutes
• On-Demand
• Free Video

Your First ML Model in Production - Considerations and Examples

Explore strategies for impactful data science projects, from identifying business objectives to deploying ML models in production. Learn real-time scoring, API integration, and lifecycle management with practical examples.

• YouTube
• 46 minutes
• On-Demand
• Free Video

Understanding Content Using Deep Learning for Natural Language Processing

Explore deep learning techniques for natural language processing to enhance content understanding in video-on-demand services.

• YouTube
• 35 minutes
• On-Demand
• Free Video

Harnessing the Power of NLP - Vector Institute Industry Collaborative Project

Explore advanced NLP techniques for industry applications, overcoming challenges in model complexity and data size. Gain practical knowledge and insights from Vector Institute's collaborative project.

• YouTube
• 36 minutes
• On-Demand
• Free Video

AI and Machine Learning in Healthcare During the Coronavirus Pandemic in China

Explore machine learning applications in healthcare during China's coronavirus pandemic, focusing on practical implementations and outcomes.

• YouTube
• 43 minutes
• On-Demand
• Free Video

An Introduction to Algorithmia and GitHub Actions

Explore Algorithmia's ML lifecycle management platform and learn to efficiently deploy models in production environments using GitHub Actions.

• YouTube
• 58 minutes
• On-Demand
• Free Video

Neural-Symbolic AI for Creativity, Generalization and Transfer Learning

Explore neural-symbolic AI's potential for enhanced transfer learning, generalization, and creativity in AI applications like personalized medicine and robotics.

• YouTube
• 43 minutes
• On-Demand
• Free Video

Responsible AI: Challenges and Emerging Practices - Keynote

Explore challenges and emerging practices in responsible AI, including unintended consequences, model interpretability, fairness, and engineering objectives.

• YouTube
• 27 minutes
• On-Demand
• Free Video