Enterprise AI: How To Operationalize Data Analytics At Scale
summary
Shows two real enterprise patterns and what actually worked: an internal AI CoE that centralizes governance and vendors (reduces risk but becomes a bottleneck) versus an AI platform team that builds a tiered self‑service platform for embedded data science teams (boosts throughput but needs more in‑house engineering). Implements Jupyter + Metaflow + S3 + GitLab for notebooks, Cloudera for self‑service, OpenShift/Azure for deployment, and enforces GDPR/data‑access rules. Claims repeatable delivery for tens–hundreds of analytics apps when governance, CI/CD and software engineering skills are present.
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chapters (7)
- 0:00Introduction and speaker intro
- 1:30Starting Enterprise AI at scale
- 3:59Scaling AI beyond first POC
- 7:38Enterprise AI framework domains
- 15:28Case study internal AI center
- 21:53Case study AI platform team
- 28:38Summary and Q&A
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