In this session, we will explore how Artificial Intelligence (AI) and Machine Learning (ML) are increasingly shaping and integrating into the Cloud Native Computing Foundation (CNCF) landscape.
We'll begin with a high-level breakdown of the CNCF landscape, highlighting its core pillars and the role of cloud-native technologies. From there, we will dive into how AI/ML workloads are supported within this ecosystem, examining where these emerging workloads naturally align with cloud-native patterns.
The session will also provide an overview of key CNCF and ecosystem projects that are enabling scalable and efficient AI/ML workflows—such as Kubeflow, Ray, and Flyte—and how these tools are helping developers, data scientists, and MLOps teams to build, deploy, and manage machine learning models in cloud-native environments.
》 Attendees will gain insights into:
1️⃣ The intersection of AI/ML and cloud-native computing
2️⃣ CNCF projects tailored for ML workflows
3️⃣ Best practices for deploying AI/ML in Kubernetes-native environments
✅️ This session is ideal for cloud-native enthusiasts, DevOps engineers, MLOps practitioners, and anyone interested in the convergence of AI and cloud-native technologies.
