Practical Evolution of Data Science with GenAI
summary
Generative AI shifts data science from prediction to creation and shows concrete outcomes: models like GPT 4.5 and IBM Granite model generate realistic customer personas (example: “Sandy,” 28) that replace costly focus groups, while image augmentation via IBM Maximum Visual Expect produces enough synthetic training data to simulate equipment failures, detect threats, and optimize lift usage. Workflows report routine cleaning and feature engineering becoming automated, iterative fine‑tuning and human validation remaining necessary, and multimodal plus RL techniques improving results over time.
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chapters (8)
- 0:00Introduction and Speaker Introductions
- 1:04Overview of Generative AI
- 1:35Traditional AI vs Generative AI
- 2:10Shifting AI Workflows
- 4:44Challenges and Concerns in Data Science
- 6:05Generative AI in Action: Case Studies
- 13:12Future Trends in Generative AI
- 14:53Conclusion and Call to Action
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