The promise of a composable AI platform is powerful: assemble best-of-breed tools to empower data scientists and AI engineers. The reality is often a path filled with hidden complexities, integration friction, and operational nightmares that pit innovation against stability.

This panel moves beyond the hype to share real-world lessons from the trenches. We will tackle the central conflict: How do you provide flexibility for ML experimentation without sacrificing the security, governance, and reliability required for production?

Join us to explore the successful patterns—and painful anti-patterns—for building these platforms. You'll leave with practical, unfiltered advice on what it truly takes to bridge the gap from an architectural dream to a production reality that works for everyone.