Speaker: Ashutosh Bhakare

Speaker: Nandakumar Vadivelu

Speaker : Ganesh Kumar Kasiviswanathan | Description : As enterprises race to infuse intelligence into every layer of their applications-it is now a critical driver of digital transformation across industries. But realizing Al at scale requires more than powerful models; it demands a resilient, flexible, and automated platform foundation. In this session, we'll explore how Kubernetes empowers organizations to adopt and operationalize Al/ML, from model training to large-scale Inference, all while managing data securely and efficiently.

We will demystify the architecture of cloud native Al stacks, covering how Kuber-netes orchestrates data pipelines, model training workloads, and inference services across hybrid and multi-cloud environments. You'll discover how tools like Kube-flow, MLflow, KServe and vLLM streamline the process of building, versioning, and serving models - enable robust MLOps workflows and accelerate Al innovation.

Attendees will learn:

The core building blocks of a cloud native Al platform

How Kubernetes serves as the backbone for scalable, portable Al workloads

Proven patterns for designing end-to-end model training and deployment pipelines

Real-world use cases demonstrating accelerated Al delivery through cloud native platforms

Whether you're an architect, data scientist, or platform engineer, this talk will equip you with practical insights to harness Kubernetes as your engine for intelligent, cloud native applications.