Kazuki Yamamoto
NTT CCT(Center for Computing Technologies), Research Engineer
Tokyo, Japan
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Yamamoto Kazuki is a research engineer at NTT Software Innovation Center, engaging in distributed systems and virtualization.
He has researched computing technology, optimizing compiler, and worked on CI/CD tasks.
Currently, he focuses on IOWN (Innovative Optical and Wireless Network) and AI Computing Platform projects.
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Effortlessly Build High-Performance AI/ML Processing Pipelines Within The ML Lifecycles
Join us for an enlightening presentation on effortlessly building an advanced processing form called "processing pipeline "for AI/ML workloads.
In streaming processing, accelerators are assigned only to specific tasks in the workload. However, we can build high-performance processing infrastructure at the service level by assigning each task to a suitable accelerator and chaining them together.
Native Kubernetes is a popular choice for deploying AI/ML workloads. However, more is needed to deploy a new processing form: the "processing pipeline" chaining accelerators.
This presentation will demonstrate how effortlessly a video inference system can be built using the "processing pipeline", which leverages Numaflow and "Dynamic Resource Allocation" (DRA). It will also introduce the "processing pipeline "that can be integrated into MLOps through Kubeflow Pipelines.
You will see a glimpse of future innovations, including high-speed communication between accelerators via the 2nd NIC.
Effortlessly Build High-Performance AI/ML Pipelines With Accelerator Chaining and K8s Native Tech
Join us for an enlightening presentation on effortlessly building advanced, high-performance processing infrastructure for AI/ML workloads with low power consumption.
In streaming processing, accelerators are assigned only for specific tasks in the workload. By assigning each task to the appropriate accelerator and "chaining" them, we obtain high-performance infrastructure with low power consumption at the service level.
Native Kubernetes is a popular choice for deploying AI/ML workloads. However, more is needed to create a new processing form, described above, "Accelerator Chaining Pipelines."
This presentation will demonstrate how we leverage Numaflow and "Dynamic Resource Allocation"(DRA) to overcome challenges, and effortlessly build an "Accelerator Chaining Pipeline" in NTT. You will see a glimpse of future innovations, including direct data transfer and CNI extensions for high-speed communication between accelerators.
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