The Path to Machine Learning & AI

On this livestream from KubeCon CloudNativeCon China, we're sitting down with Alejandro Saucedo, Chief Scientist at the Institute for Ethical AI & Machine Learning and Dr. Han Xiao, Engineering Lead at Tencent AI Lab to learn more about how Kubernetes is used in an AI&ML context. When one is running complicated AI/ML workloads at scale, Kubernetes fits naturally as the solution due to its ability to scale rapidly, portability, and the variety of tools available for AI & ML use cases on Kubernetes, Kubeflow. Rather than setting out to recreate the wheel, Kubeflow offers those working with AI & ML data sets the best-of-the-best options for deploying AI/ML workloads on Kubernetes by bringing together Jupyter notebooks, TensorFlow model training to adjust CPU & GPU cluster size for workloads, TensorFlow serving containers to export trained models to Kubernetes, and Kubeflow Pipelines.