Kubernetes AI Day North America 2021
as the organisers put it
register Sponsor explore the schedule That’s a wrap on Kubernetes AI Day at KubeCon + CloudNativeCon NA 2021! Thank you to all the attendees, sponsors, and speakers that joined us virtually this year.
who it's for
MLOps engineers, machine learning engineers, platform engineers, data scientists, site reliability engineers, security engineers, frontend engineers
topics
Kubeflow Pipelines v2; model ops continuous pipelines; federated learning with FATE; pod security admission restricted profile; Argo Workflows for R forecasting; Kubeflow web apps and namespace isolation; adversarial robustness with ART; AIOps for CI with Kubeflow Pipelines; KServe model mesh serving; Kale notebook-to-pipeline integration; containerd sandbox and nerdctl features
| title | author | length |
|---|---|---|
| Kubeflow Pipelines v2: The Next Generation of MLOps on Ku... | Karthik Ramachandran | 09m |
Kubeflow Pipelines v2 surfaces metadata and metrics as first-class objects with a standardized ontology and queryable artifact lineage, adds richer metric… | ||
| Evolving with Kubernetes: Embracing Model Ops | Steven Huels | 06m |
Claims Kubernetes can host the full ML lifecycle — data acquisition, cleansing, model development, packaging into images and deployment — and does so using… | ||
| What, Why and How of Federated Machine Learning – Implementation Using... | Neeraj Arora, Layne Peng | 21m |
Deploys a two‑party FATE cluster on Kubernetes with KubeFATE and a FATE Operator for Kubeflow, trains a homo logistic regression on a Kaggle credit‑card fraud… | ||
| Security Best Practices for AI on Kubernetes | Guy Salton | 21m |
Shows how to secure AI workloads on Kubernetes by creating service account per researcher and one namespace each, mounting per-user NFS directories via PV/PVC… | ||
| Making Complex R Forecast App... | Natalia Costa Araujo, Pedro Szloma Herr Zaterka, Matheus Sesso Gay | 15m |
Built a Forecast-as-a-Service that keeps R for modeling but moves orchestration to Argo Workflows on Kubernetes, using GCP Pub/Sub, Prometheus/Grafana and… | ||
| Case Study: Developing and Scaling Kubeflow’s Web Apps | Andrey Velichkevich, Kimonas Sotirchos | 21m |
Kubeflow maintains at least five web apps (Notebooks, PVCs, Tensorboards, Models, AutoML experiments) that perform CRUD on K8s objects and scale by adding… | ||
| Defending Against Adversarial Model Attacks Using Kubeflow | Animesh Singh, Andrew Butler | 21m |
Built pipelines that embed Adversarial Robustness Toolbox (ART) into Kubeflow Pipelines and a KFServing explainer to run attacks and defenses automatically… | ||
| AIOps for CI with Kubeflow Pipelines | Oindrilla Chatterjee, Aakanksha Duggal | 26m |
An open source AIOps system called AI4CI automates Jupyter notebooks with Kubeflow Pipelines to collect CI data from Prow and TestGrid, calculate concrete… | ||
| Serving Machine Learning Models at Scale Using KServe | Yuzhui Liu | 31m |
Explains how KServe replaces one-model-one-service with a Model Mesh multi-model serving design that co-locates many models in a single runtime, integrating… | ||
| A Better and More Efficient ML Experience for CERN Users | Ricardo Rocha, Dejan Golubovic | 24m |
CERN runs a centralized Kubeflow service that pools GPUs, FPGAs and cloud TPUs to replace many siloed group clusters, improving utilization and enabling bursts… | ||
| Closing Remarks | Thiago Bittencourt Gil | 04m |
Program committee members highlight that science methods ground AI discussions and that recent disruption forces companies to cut spending while needing… | ||
| containerd Introduction and Deep Dive | Phil Estes, Maksym Pavlenko, Derek McGowan, Mike Brown | 31m |
containerd reports 29 releases across three branches since 2019, automates CI on GitHub Actions, and ships official Windows packaging while adding arm64… | ||
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