This session presents a practical blueprint for building a SageMaker-like AI Factory using only upstream open source projects.
We walk through a complete architecture that combines Kubernetes, Kubeflow, MLflow, KServe, vLLM, and a modern Cloudscape-based console, secured with Keycloak and FreeIPA for enterprise-grade IAM and SSO.
On the data side, we leverage Ceph, Apache Iceberg/Hudi, Kafka, Spark/Flink, and Feast to create a robust lakehouse and feature platform. We then show how to orchestrate the full ML lifecycle—from data ingestion and feature engineering to training, model registry, deployment, monitoring, and cost visibility—using GitOps, Prometheus/Grafana, OpenCost, and policy-as-code.
Attendees will leave with a clear, vendor-neutral reference architecture and a concrete checklist of upstream components to assemble their own open, portable, and sovereign AI Factory across on-prem, cloud, and edge environments.
