Clement Farabet – NVIDIA: Scaling ML OPs for AV Development

Savin Goyal – Outerbounds: Productivity in Machine Learning

Keshav Pingali – Katana Graph: Knowledge Graphs, Graph AI, & The Need For High-performance Computing

Lenore & Manuel Blum – Carnegie Mellon: A Theoretical CS Perspective on Consciousness

Dmytro Dzhulgakov – Meta AI: Scaling AI Through Usability - Lessons from PyTorch

Chris Rossbach – Katana Graph: Technology Overview

Simon Knowles – Graphcore: About that "Brain Scale" AI

Benjamin Harvey – AI Squared: Accelerating AI/ML Integration into Production Business Applications

Wei Li – Intel: Scaling AI with Performance, Productivity, and Security

Stewart Hall – Cerebras Systems: Training of Giant Neural Networks with Weight Streaming

Matei Zaharia – Stanford: Scaling up NLP - Bigger Systems or Smarter Models?

David Aronchick – The SAME Project: A Cloud Native Approach to Reproducible Machine Learning

Nicoló Fusi – Microsoft Research: Data-centric AI

Richard Socher – You.com: Summarizing the Web with NLP

Even Oldridge – NVIDIA Merlin: Recommender Systems at Scale on the GPU

Alyona Medelyan – Thematic: How to Start a Startup for AI Engineers

Peter Mattson – MLCommons: Building What ML Needs

Ion Stoica – Ray: A Universal Framework for Distributed Systems

Chris Hadfield interviewed by Reza Zadeh.

Matroid Product Overview

David Aronchick - Using MLOps to Bring ML to Production

Josh Romero - Scaling DL on the Summit Supercomputer

Panel Discussion - AI Products & MLOps

Peter Mattson - MLPerf: Driving Innovation by Measuring Performance

Savin Goyal - Human-Centric Approach to ML Infrastructure

Wes McKinney - Apache Arrow: Present & Future

Mohamed Fawzy - Distributed Training at Facebook Scale

Kunle Olukotun - Accelerating Software 2.0

Sean Lie - Wafer-Scale ML

Matei Zaharia - Scaling ML Development with MLflow