Explore the development and impact of Uber's machine learning education program in this 58-minute podcast episode featuring Melissa Barr and Michael Mui. Gain insights into the program's design, implementation, and outcomes, including how it teaches employees to use machine learning both generally and specifically for Uber. Learn about the engineering principles applied to course design, making it applicable to other products. Discover how the platform team leverages user feedback to enhance the system, and understand the challenges and successes of expanding ML knowledge within a large organization. Delve into topics such as reproducibility best practices, course effectiveness evaluation, and the future of ML education at Uber, including potential modules on generative AI and ChatGPT.
Machine Learning Education at Uber - Developing an Internal ML Platform
Overview
Syllabus
[] Melissa and Michael's preferred coffee
[] Takeaways
[] Please subscribe to our newsletters and leave reviews on our podcasts!
[] Machine learning at Uber education program
[] The Uber courses
[] Tailoring the Uber education system
[] Growing out of the ML-Ed platform efforts
[] Expanding the ML Market Size
[] Relationship evolution
[] Reproducibility best practices
[] Learning development timeline
[] Courses effectiveness evaluation
[] Tracking Progress Challenge
[] ML platforms for internal tools
[] Impact of ML Education at Uber
[] Recommendations to companies who want to start an ML-Ed platform
[] Early ML Adoption Program
[] Homegrown or home-built platform
[] Feature creation to a course
[] ML Education at Uber: Frameworks Inspired by Engineering Principles
[] The Future of ML Education at Uber
[] Unclear ways to spread ML knowledge
[] Module for Generative AI and ChatGPT
[] Measurement of success
[] Wrap up
Taught by
MLOps.community
