MLOps

Conferences

• OPML ‘19
• OPML ‘20

Companies

• Databricks
  • Stanford MLSys #2: Matei Zaharia: Machine Learning at Industrial Scale: Lessons from the MLflow Project (2020)
  • Stanford MLSys #65: Alkis Polyzotis: What can Data-Centric AI Learn from Data and ML Engineering? (2022)
• Google
  • How ML Breaks: A Decade of Outages for One Large ML Pipeline
  • Stanford MLSys #63: Arjun Akula: Improving Robustness and Interpretability in Vision and Language Grounding Models (2022)
• META
  • OCPSummit19: Facebook AI Infrastructure (2019)
  • Stanford MLSys #66: Roman Kazinnik: Machine Learning in Production (2022)
• Netflix
  • Runway - Model Management at Netflix (2020)
  • MLsys Stanford Seminar: Savin Goyal: How Netflix does MLSys
• NVIDIA
  • Inside NVIDIA’s AI Infrastructure for Self-driving Cars (2020), OPML ‘20 presentation of Maglev (NVIDIA’s AI platform)
  • Scalable Active Learning for Autonomous Driving, a practical implementation and A/B Test (2019)
• Tesla
  • Tesla AI Day 2021, overview of their full stack
• Uber
  • Meet Michelangelo: Uber’s Machine Learning Platform (2017)
  • Evolving Michelangelo Model Representation for Flexibility at Scale (2019)
• Waymo
  • Offboard 3D Object Detection from Point Cloud Sequences, Ch. Qi et al, Waymo (2021), video, explaining Waymo’s solution for auto labeling data

Other

• Machine Learning
• Probabilities and Statistics
• Reinforcement Learning
• Robotics
• Self Driving Cars