January 29, 2023 7:00 PM PST


This meeting discusses the architecture and development cycle of machine learning systems. It covers the importance of understanding both software engineering and machine learning engineering, the challenges faced in the field, and the future of machine learning and AI.

Presenter: Coach He, Machine Learning Director


Meeting Summary
Machine Learning Architecture and Development Cycle
Common Issues
Key Challenges in Machine Learning
Machine Learning Applications
Basic Steps for ML System Development
  1. Modeling:
    • Collect and clean historical data.
    • Develop and train models.
    • Validate and evaluate models.
  2. Deployment:
    • Deploy models to production.
    • Monitor and update models and data.
Challenges from ML Systems
Solutions to ML System Challenges
Data Infrastructure
Development Lifecycle
Future of Machine Learning
Recommended Resources