Vijay Vammi

How do leading enterprises build AI platforms that go beyond isolated pilots and enable scalable, production-grade AI applications? In this panel, senior platform and infrastructure leaders share practical insights into architecting AI platforms that deliver reusability, governance, and speed, at scale.

• Designing reusable, composable AI platforms for enterprise-wide adoption
• Striking the balance between centralized control and developer autonomy
• Tooling and orchestration strategies for managing the end-to-end model lifecycle
• Infrastructure choices for supporting hybrid and multi-cloud AI workloads
• Embedding governance, performance, and cost-efficiency into platform design

Vijay Vammi, Enterprise MLOps Services & Technology Owner at Astrazeneca join us to share his team’s journey to build scalable, observable, and developer-friendly platforms that balance innovation with control. Moving the business towards MLOps to better align engineers with your AI platform infrastructure.

• Enabling product velocity while protecting innovation through scalable ML pipelines
• Embedding observability and performance monitoring into every stage of the model lifecycle
• Unifying workflows through a single internal developer platform to reduce silos and increase adoption
• Driving adoption of GenAI and platform standards through internal consulting, prompt design, and user-centric tooling
• What does success look like? Measuring impact for your AI and ML investments.