pipelines

Cloud AI Platform Pipelines appear to use Kubeflow Pipelines on the backend, which is open source [1] and runs on Kubernetes. The Kubeflow team has invested a lot of time on making it simple to deploy across a variety of public clouds [2], [3]. If Google were to kill it, you could easily run it on any other hosted Kubernetes service. I haven't used Cloud AI Platform Pipelines, but have spent a lot of time working with Kubeflow Pipelines and its pretty great! [1] https://github.com/kubeflow/pipelines [2] https://www.kubeflow.org/docs/aws/ (Deploy to AWS) [3] https://www.kubeflow.org/docs/azure/ (Deploy to Azure)

Who cares about this garbage if the tool isn't even open source? There are lots of ML deployment tools that are open source. I know haters will downvote my post, but it's the truth. If I can't actually fork and evaluate a tool, it is hyped up garbage to me. Meanwhile, here is a list of open source ML deployment packages: https://github.com/oracle/graphpipe https://github.com/eliorc/denzel https://github.com/tensorflow/serving https://github.com/ucbrise/clipper https://github.com/DLHub-Argonne/dlhub_sdk https://github.com/kubeflow/pipelines

FYI, Google's new Kubeflow Pipelines service uses Argo. https://cloud.google.com/blog/products/ai-machine-learning/i... https://github.com/kubeflow/pipelines#acknowledgments