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KServe is a cloud-native open source project for serving production ML models built on CNCF projects like Knative and Istio. In this talk, we’ll update you on KServe’s progress towards 1.0, the latest developments, such as ModelMesh and InferenceGraph, and its future roadmap. We’ll discuss the Kubernetes design patterns used in KServe to achieve the core ML inference capability, as well as the design philosophy behind KServe and how it integrates the CNCF ecosystem so you can walk up and down the stack to use features to meet your production model deployment requirements. The well-designed InferenceService interface encapsulates the complexity of networking, lifecycle, server configurations and allows you to easily add serverless capabilities to model servers like TensorFlow Serving, TorchServe, and Triton on CPU/GPU. You can also turn on full service mesh mode to secure your InferenceServices. We’ll walk through different scenarios to show how you can quickly start with KServe and evolve to a production-ready setup with scalability, security, observability, and auto-scaling acceleration using CNCF projects like Knative, Istio, SPIFFE/SPIRE, OpenTelemetry, and Fluid.
Dan Sun is a team lead of the Data Science Serverless Runtime team at Bloomberg. Focused on building mission-critical production ML inference managed solutions, he strives to understand and tackle data scientists' complex problems. He also has many years of experience at Bloomberg... Read More →
Theofilos is a maintainer of KServe project as well as contributor on Kubeflow. He works as a Sr. ML architect in Amazon Web Services (AWS) Data Lab, with AWS customers who want to build and run models using MLOps principles.
