Kubernetes is a popular open-source software, today’s de-facto open standard, to run production workloads. Innovation in the ecosystem thrives with multiple projects that enrich the platform’s core. Machine Learning Operations (MLOps) is part of the same effort to bring automation into Kubernetes for ML pipelines.
This session aims to explore the Kubernetes-native tools available such as Tekton, Argo CD, Kubeflow, and OpenDataHub, to apply DevOps and GitOps principles in AI/ML contexts.
By the end of the session, attendees will have a clear understanding on how to automate and simplify the iterative process of integrating ML models into software development processes, production rollout, monitoring, retraining, and redeployment for continued prediction accuracy with open source tools.
