Machine Learning in Fastly's Compute@Edge
Offered By: Linux Foundation via YouTube
Course Description
Overview
Explore the integration of machine learning models in Fastly's Compute@Edge environment through WebAssembly modules and wasi-nn in this conference talk. Discover the advancements made to enable efficient execution in a stateless FaaS environment, including extensions to the wasi-nn spec, revisions to host APIs, security-related tradeoff considerations, and the introduction of a new proxy backend based on the KServe protocol. Witness a demonstration of these functionalities through a Compute@Edge service utilizing OpenVINO, ONNX, and PyTorch for classification and generative AI applications.
Syllabus
Machine Learning in Fastly's Compute@Edge - Andrew Brown, Intel & Matthew Tamayo-Rios, Fastly
Taught by
Linux Foundation
Tags
Related Courses
Serverless Machine Learning Model Inference on Kubernetes with KServeDevoxx via YouTube ModelMesh: Scalable AI Model Serving on Kubernetes
Linux Foundation via YouTube MLSecOps - Automated Online and Offline ML Model Evaluations on Kubernetes
Linux Foundation via YouTube Creating a Custom Serving Runtime in KServe ModelMesh - Hands-On Experience
Linux Foundation via YouTube Integrating High Performance Feature Stores with KServe Model Serving
Linux Foundation via YouTube