Kubeflow's Integration with Ksonnet and Community Involvement

This highlights one more option in Kubeflow - the ability to pass in inputs based on your deployment. This command creates a tf-serving service on the GKE cluster, and makes it available to your application. For more information about of deploying and monitoring TensorFlow training jobs and TensorFlow models please refer to the [user guide](https://github.com/google/kubeflow/blob/master/user_guide.md). ## Kubeflow + ksonnet One choice we want to call out is the use of the ksonnet project. We think working with multiple environments (dev, test, prod) will be the norm for most Kubeflow users. By making environments a first class concept, ksonnet makes it easy for Kubeflow users to easily move their workloads between their different environments. Particularly now that [Helm is integrating ksonnet](https://blog.heptio.com/ksonnet-intro-43f6183a97a6) with the next version of their platform, we felt like it was the perfect choice for us. More information about ksonnet can be found in the ksonnet [docs](https://ksonnet.io/). We also want to thank the team at [Heptio](https://heptio.com/) for expediting features critical to Kubeflow's use of ksonnet. ## What’s Next? We are in the midst of building out a community effort right now, and we would love your help! We’ve already been collaborating with many teams - [CaiCloud](https://caicloud.io/article_detail/5a3b58fce928ca1c69e1aa70), [Red Hat & OpenShift](https://blog.openshift.com/machine-learning-openshift-kubernetes/), [Canonical](https://tutorials.ubuntu.com/tutorial/get-started-kubeflow), [Weaveworks](https://www.weave.works/blog/kubeflow-and-weave-cloud), [Container Solutions](http://container-solutions.com/tensorflow-on-kubernetes-kubeflow/) and many others. [CoreOS](https://coreos.com/), for example, is already seeing the promise of Kubeflow: “The Kubeflow project was a needed advancement to make it significantly easier to set up and productionize machine learning workloads on Kubernetes, and we anticipate that it will greatly expand the opportunity for even more enterprises to embrace the platform. We look forward to working with the project members in providing tight integration of Kubeflow with Tectonic, the enterprise Kubernetes platform.” -- Reza Shafii, VP of product, CoreOS If you’d like to try out Kubeflow right now right in your browser, we’ve partnered with [Katacoda](https://www.katacoda.com/) to make it super easy. You can try it [here](https://www.katacoda.com/kubeflow)! And we’re just getting started! We would love for you to help. How you might ask? Well… - Please join the[slack channel](https://join.slack.com/t/kubeflow/shared_invite/enQtMjgyMzMxNDgyMTQ5LWUwMTIxNmZlZTk2NGU0MmFiNDE4YWJiMzFiOGNkZGZjZmRlNTExNmUwMmQ2NzMwYzk5YzQxOWQyODBlZGY2OTg) - Please join the[kubeflow-discuss](https://groups.google.com/forum/#!forum/kubeflow-discuss) email list - Please subscribe to the[Kubeflow twitter](http://twitter.com/kubeflow) account - Please download and run kubeflow, and submit bugs! Thank you for your support so far, we could not be more excited! Note: * This article was amended in June 2023 to update the trained model bucket location.

This section highlights Kubeflow's ability to pass deployment-specific inputs, exemplified by creating a tf-serving service on GKE. It emphasizes the choice of Ksonnet for managing multiple environments (dev, test, prod), citing its integration with Helm. It then extends an invitation to join the Kubeflow community and mentions the collaboration of Kubeflow with other parties, and provides resources for getting started with Kubeflow, including a Katacoda tutorial and various community channels.