Does GenAI "belong" to data scientists?
summary
Argument: GenAI often lands on ML platform teams by habit but that misplaces value because Anthropic and OpenAI already run the data pipeline and most work now is changing inputs not retraining. The concrete leftovers are prompt and context engineering, distributed systems for multi‑agent execution, large human annotation workflows, occasional fine‑tuning of open‑source models, and building evals and observability pipelines that measure functional behavior in production. The verdict: build cross‑functional teams.
AI-written summary · about
chapters (10)
- 0:00GenAI and agentic development
- 1:18Speaker and BrainTrust overview
- 3:02Enterprise vs AI-native agent approaches
- 5:10ML versus GenAI differences
- 7:58Agents belonging to data scientists
- 9:24Counterarguments on agent ownership
- 11:10Non-data scientists leading agents
- 12:37Data scientists' value in agents
- 14:20Team composition for agents
- 15:24Conclusion and Q&A
related talks
01h 01mAre Your Engineering Teams Prepared for the GenAI Revolution
11mOpen source GenAI vs. enterprise GenAI: Navigating the future of AI innovation
28mIs GenAI the technology revolution we need? How can we harness it at work?
41mGenerative AI: bliss or miss?
29mWhen to Gen AI? A strategic framework for real-world AI adoption
50mStrategic Importance of GenAI Platforms for Business Competitiveness
18mStructuring a modern AI team
36mGenAI governance in practice