ReadMeFeed / Community knowledge
Real questions. Useful conversations. Find the people who know your stack.
Typed artifact passing in Kubeflow Pipelines v2 is a runtime contract, not a compile-time check. Learn how OutputPath and InputPath resolve against cluster storage, and how to verify your pipeline setup before it fails.
Stop running monolithic ML scripts. Learn how to use Kubeflow Pipelines' Lightweight Python Components to build modular, cached, and scalable ML workflows.
Stop relying on implicit Keras exports. Learn how to use TensorFlow SavedModel signatures to create a stable contract between your training pipeline and production inference.
A practical pattern for wrapping ML scripts as typed KFP v2 components with pinned images, plus local compile checks and cluster verification steps.
Learn how to build modular, containerized ML workflows using the Kubeflow Pipelines SDK, focusing on component creation, DAG orchestration, and artifact management.
A concise architecture note for serving TensorFlow models with SavedModel and TensorFlow Serving: minimal design, trust boundaries, operational checks, failure modes and conditions that trigger a redesign.
Data scientists often copy‑paste preprocessing code, causing drift. This guide shows how to turn a CSV scaler into a reusable Kubeflow Pipeline component, compile it to Argo YAML, and run it on a cluster, ensuring reproducibility and CI/CD integration.
Kubeflow Pipelines pays off when you need caching, lineage, and scheduled retraining on Kubernetes — but a plain CronJob is often the cheaper right answer. Here's how to decide, with a working KFP v2 example.
Learn how to create immutable, version‑controlled Kubeflow Pipeline components, pass artifacts, and avoid common pitfalls. A step‑by‑step example with Docker build, registry push, and pipeline run is included.
kubeflow
python
kubernetes
keras