This page contains a list of presentations and demos. If you'd like to add a presentation or demo here, please send a pull request.
Presentation/Demo |
Presenters |
Distributed Machine Learning Patterns |
Yuan Tang |
KubeCon 2019: Introducing KFServing: Serverless Model Serving on Kubernetes |
Dan Sun, Ellis Tarn |
KubeCon 2019: Advanced Model Inferencing Leveraging KNative, Istio & Kubeflow Serving |
Animesh Singh, Clive Cox |
KubeflowDojo: KFServing - Production Model Serving Platform |
Animesh Singh, Tommy Li |
NVIDIA: Accelerate and Autoscale Deep Learning Inference on GPUs with KFServing |
Dan Sun, David Goodwin |
KF Community: KFServing - Enabling Serverless Workloads Across Model Frameworks |
Ellis Tarn |
KubeflowDojo: Demo - KFServing End to End through Notebook |
Animesh Singh, Tommy Li |
KubeflowDojo: Demo - KFServing with Kafka and Kubeflow Pipelines |
Animesh Singh |
Anchor MLOps Podcast: Serving Models with KFServing |
David Aponte, Demetrios Brinkmann |
Kubeflow 101: What is KFServing? |
Stephanie Wong |
ICML 2020, Workshop on Challenges in Deploying and Monitoring Machine Learning Systems : Serverless inferencing on Kubernetes |
Clive Cox |
Serverless Practitioners Summit 2020: Serverless Machine Learning Inference with KFServing |
Clive Cox, Yuzhui Liu |
MLOps Meetup: KServe Live Coding Session |
Theofilos Papapanagiotou |
KubeCon AI Days 2021: Serving Machine Learning Models at Scale Using KServe |
Yuzhui Liu |
KubeCon 2021: Serving Machine Learning Models at Scale Using KServe |
Animesh Singh |
KubeCon China 2021: Accelerate Federated Learning Model Deployment with KServe |
Fangchi Wang & Jiahao Chen |
KubeCon AI Days 2022: Exploring ML Model Serving with KServe |
Alexa Nicole Griffith |
KubeCon AI Days 2022: Enhancing the Performance Testing Process for gRPC Model Inferencing at Scale |
Ted Chang, Paul Van Eck |
KubeCon Edge Days 2022: Model Serving at the Edge Made Easier |
Paul Van Eck |
KnativeCon 2022: How We Built an ML inference Platform with Knative |
Dan Sun |
KubeCon EU 2023: The state and future of cloud native model serving |
Dan Sun, Theofilos Papapanagiotou |