Where do data science, AI and software development converge and diverge?
summary
IBM's data science community focuses on operational AI: moving models into production with Python, pandas, scikit‑learn, AutoML and ModelOps while tackling monitoring, retraining, bias and explainability. Growth metrics jump from under 1,000 to 12,423 members after a Coursera partnership and virtual events; practitioners attend over 25% of sessions. Team authorship yields dozens of learning artifacts and 78 content pieces, and a masterclass plus hackathon produces an autism screening app as a working proof of concept.
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chapters (5)
- 0:00Introduction to convergence
- 3:52AI lifecycle complexity
- 8:49Role distinctions in AI
- 13:27Measuring community success
- 20:51Key takeaways and Q&A
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