Google Cloud has debuted the alpha release of its AI Hub as part of its mission to accelerate AI project development in enterprises.
The AI hub acts as a centralized hub where users can access ready-made machine learning (ML) pipelines, application documentation and TensorFlow modules, aimed at speeding up the time it takes companies to get their AI projects off the ground. This means that users will have access to the high-quality ML resources that have been developed by Google Research, Google Cloud AI and other Google teams.
Google Cloud are also encouraging companies to use the AI Hub as a private storage repository for their own AI and ML resources, allowing them to be shared with the whole business.
Google also announced launch of Kubeflow Pipelines, a new element of Kubeflow a leading open source project that Google spearheaded.
"Kubeflow Pipelines provides a workbench to compose, deploy and manage reusable end-to-end machine learning workflows, making it a no lock-in hybrid solution from prototyping to production," explained Hussein Mehanna, engineering director of the Cloud ML platform at Google.
"It also enables rapid and reliable experimentation, so users can try many machine learning techniques to identify what works best for their application."
These are two of a number of initiatives Google are pushing in an attempt to democratize AI in order to propel innovation. The tech giant is attempting "to make it possible for everyone in the world to use AI and to build great models for their purposes," said Rajen Sheth, senior director of product management of Google’s Cloud AI, at a press conference in July.