Google Cloud introduces pipelines for those beyond ML prototyping • DEVCLASS

The Google Cloud team just celebrated the beta launch of its AI Platform Pipelines feature with a couple of additions and improvements to the machine learning workflow execution environment. The product was started to provide those at the beginning of their machine learning journey with a way to "deploy robust, repeatable machine learning pipelines along with monitoring, auditing, version tracking, and reproducibility" in an "easy to install, secure" environment. It is therefore mainly made up of the infrastructural component needed to run the workflows, as well as tools for creating and sharing pipelines. Since the service is part of Google Cloud, it can be quickly installed via the company's cloud console, which also takes care of access management. Options for the building pipelines part boil down to the Kubeflow Pipelines SDK, which isn't surprising given that the AI Platform Pipelines run on a GKE cluster, and the development kit for TensorFlow Extended (TFX), TF's end-to-end machine learning platform.