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Bloomberg provides an on-premises Data Science Platform using cloud-native software to support internal AI model training. It runs on Kubernetes spanning multiple data centers and featuring a diverse range of GPU types. However, managing such a large-scale and heterogeneous GPU environment poses many challenges, such as improving resource utilization, reducing operational costs, and scheduling workloads across different GPU types. In collaboration with the Karmada community, Bloomberg's Data Science Platform team has aimed to tackle these challenges by addressing multi-cluster batch job management problems. This talk will delve into the approaches the team has adopted, including: - Intelligently scheduling GPU workloads across multiple clusters - Using Karmada's resource interpreter to support Custom Resource Definitions (CRDs) on top of a multi-cluster architecture - Building a highly available Karmada control plane - Establishing a consistent training job submission interface
Yao Weng is a Senior Software Engineer on Bloomberg’s Data Science Platform engineering team. She has contributed extensively to optimizing the company’s Kubernetes environment for high performance compute, model inference, and workflow orchestration. Yao Weng obtained her Ph.D... Read More →
Leon Zhou is a software engineer on the Data Science Platform engineering team at Bloomberg. With prior NLP experience, he is now building ML platforms to facilitate machine learning development. He is interested in ML infrastructure to enable large-scale training and complex pipelines... Read More →
The emergence of Generative AI (GenAI) has introduced new challenges and demands in AI/ML inference, necessitating advanced solutions for efficient serving infrastructures. The recently created Kubernetes Working Group Serving (WG Serving) is dedicated to enhancing serving workload on K8s, especially for hardware-accelerated AI/ML inference. This group prioritizes compute-intensive inference scenarios using specialized accelerators, benefiting various serving workloads such as web services and stateful databases. This session will dive into WG Serving's initiatives and workstreams. We will spotlight discussions and advancements in each workstream. We are also actively looking for feedback and partnership with model server authors and other practitioners who want to utilize powers of K8s for their serving workloads. Join us to gain insight into our work and learn how to contribute to advancing AI/ML inference on K8s.
Yuan is a principal software engineer at Red Hat, working on OpenShift AI. Previously, he has led AI infrastructure and platform teams at various companies. He holds leadership positions in open source projects, including Argo, Kubeflow, and Kubernetes. He's also a maintainer and... Read More →
Eduardo is a Senior Systems Software Engineer at NVIDIA, working on the Cloud Native Technologies team. Eduardo has focused on enabling users to build and deploy containers on distributed environments.
In this session, KubeEdge project maintainers will provide an overview of KubeEdge's architecture and its industry-specific use cases. The session will begin with a brief introduction to edge computing and its growing importance in IoT and distributed systems. The maintainers will then delve into the core components and architecture of KubeEdge, demonstrating how it extends Kubernetes' capabilities to manage edge computing workloads efficiently. They will share success stories and insights from organizations that have deployed KubeEdge in various edge environments, such as smart cities, industrial IoT, edge AI, robotics, and retail, highlighting the tangible benefits and transformational possibilities. Additionally, the session will introduce the certified KubeEdge conformance test, discuss advancements in technology and community governance within the KubeEdge project, and share the latest updates on the project's graduation status.
Hongbing Zhang is Chief Operating Officer of DaoCloud. He is a veteran in open source areas, he founded IBM China Linux team in 2011 and organized team to make significant contributions in Linux Kernel/openstack/hadoop projects. Now he is focusing on cloud native domain and leading... Read More →
Yin Ding, an Engineering Manager at Google, lead the Kubernetes Hardening team and brings over 15 years of expertise in large-scale and distributed computing. As a co-founder of the CNCF KubeEdge open-source project and the TSC Chair of LF Edge Akraino, Yin Ding has made significant... Read More →
These days building so-called cloud-native apps involves assembling a custom stack of tools 10x bigger than the app we're building. Additionally, applications increasingly need to expand out to the edge and cloud-native stacks simply don't work in those environments. Fortunately with NATS, we don't need a stack. In this session you'll see how we can leverage compute, storage, and connectivity to build cloud-to-edge native apps more powerful than ever, with less code, effort, and frustration.
Byron is the VP of Product and Engineering at Synadia, the company behind the NATS.io project. Prior to joining Synadia, he spent 14 years building software and systems in support of pediatric biomedical research. Outside of work, Byron enjoys running, cooking, yard work, and spending... Read More →
Over the past ten years, gRPC has become indispensable to a breathtaking array of engineering organizations. Join the maintainers look back at how gRPC got to where it is today, the way the software has grown, and the community along with it. Then, see what's in store for the future of gRPC in the decades to come.
Israel works at Broadcom’s Enterprise Security Group as a Software Architect for the next generation Cloud Native Datapath of the Web Security Service.
Kevin works at Google as a Software Engineer Manager on the gRPC team. At Google Kevin has worked on projects such as Chromecast, Google Home, Stadia and now gRPC. Kevin is a Java Champion and four time JavaOne Rock Star. Kevin has spoken at conferences such as Google I/O, JavaOne... Read More →
