F8 2019 Day 2 keynote and session videos
May 1, 2019
Summary
This post highlights the use of PyTorch for accelerating AI research to production deployment in computer vision, NLP, and machine translation. It also details the application of ML for developer productivity tools such as code search, code recommendation, and automatic bug fixing. Additionally, it introduces adaptive experimentation as an AI-enabled testing approach for product, infrastructure, ML model, and marketing campaign optimization.
More from the AI Research and Development trail
- An Organizational Second Brain: Building an AI That Learns From Experts
- From User Sequences to Scaling Laws: A Multi-Stage Architecture for Meta’s Ads Ranking
- GEM Training: How Meta Doubled the Efficiency of Its LLM-Scale Ads Foundation Model
- Exploring Hierarchical Interest Representation For Meta Ads Deep Funnel Optimization
- Meta’s AI Storage Blueprint at Scale