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Effortlessly Build High-Performance AI/ML Processing Pipelines Within the ML Lifecycles - Kazuki Yamamoto, NTT

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.