ML stand-up patterns — what to share and how
Lesson 1 of 4 in Talking About AI at Work — Meetings, Stand-ups & Reviews · 22 minutes
ML team stand-ups have specific patterns. This lesson covers what to share, how to share it, and how to participate confidently.
What you'll learn
- Structure ML stand-up updates
- Communicate experiment status concisely
- Surface blockers effectively
- Adapt to async vs sync stand-ups
From the lesson
ML team stand-ups differ from typical software stand-ups. Experiments take days; results matter more than commits; blockers are often data or compute. This lesson covers the specific patterns ML teams use.
1. The ML stand-up — what makes it different
Software stand-ups: • Yesterday: I shipped feature X. • Today: I'm implementing feature Y. • Blockers: review on PR #123. • Daily progress visible.
ML stand-ups: • Yesterday: I trained model X with hyperparameter sweep; results are mixed. • Today: I'm investigating why validation accuracy plateaued. • Blockers: GPU queue; need data labeling for 10K examples. • Multi-day experiments common.
Key differences:
Time horizons: • Software: daily progress.…