Despite massive investments in AI, most enterprise AI projects still fail to reach production, as confirmed by recent studies from MIT and industry analysts. The failure point isn't the technology - it's the disconnect between data science teams, engineering workflows, and business stakeholders. MLOps bridges this critical gap by bringing DevOps principles to AI/ML systems, but it's more than just technical operations - it's about creating alignment between technology teams and business objectives to ensure AI delivers real value.
This talk presents strategies and thoughts for building production-ready AI systems through effective requirements gathering and MLOps practices. We'll walk through a complete end-to-end MLOps pipeline, demonstrating how automation and continuous feedback loops transform experimental models into reliable production services.
We'll leverage the CNCF ecosystem—particularly Kubeflow and cloud-native tooling—to build vendor-neutral, multi-cloud solutions that scale with enterprise needs. Whether you're getting started with MLOps or optimizing existing workflows, you'll leave with actionable strategies to overcome the most common roadblocks in AI deployment.