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technologyreview.com·2h ago
Cloud & Infrastructure
The New Stack·29d ago·5 min read
Why most AI projects fail: It’s infrastructure and people
TL;DR
Many AI projects fail due to inadequate infrastructure and a lack of understanding of the differences between prototyping and production environments. While prototypes are often experimental, the transition to production requires robust systems and skilled personnel.
✦ Why It Matters
Engineers should assess their current infrastructure and invest in tools like Kubernetes for scalable deployment.
Key Takeaways
- Choose production-ready data infrastructure for AI projects.
- Avoid vendor-managed platforms for critical applications.
- Utilize AI DBA agents to enhance database management.
- Ensure operational teams are equipped to handle increased workloads.
How It Works
AI DBA agents, like pgEdge's Ellie, assist database administrators by automating monitoring and diagnostics, allowing them to manage more databases efficiently.