MLOps at Scale: Predicting Bus Departure Times Using 18,000 ML Models

Explore a 39-minute conference talk from the Toronto Machine Learning Series (TMLS) featuring Alice Gibbons, Technical Specialist at Microsoft, and Hubert Duan, Cloud Solution Architect at Microsoft. Delve into the practical implementation of AI ethics using Aristotle's practical syllogisms as a framework. Learn how to ground ethical considerations in specific methods and tools, moving beyond abstract discussions. Discover how this approach, similar to conditional statements in programming, can be applied to everyday decision-making in AI development. Gain insights on predicting bus departure times using 18,000 ML models, showcasing MLOps at scale. Understand how the speakers connect universal ethical premises with local contexts to derive actionable conclusions, providing a structured approach to navigating ethical challenges in AI and machine learning projects.

Alice and Hubert - MLOps at Scale: Predicting Bus Departure Times using 18,000 ML Models