Google Cloud has reinforced its commitment to lowering the enterprise adoption barriers to artificial intelligence (AI) with the alpha release of AI Hub.
The offering is being pitched by the public cloud giant as a centralised hub where users can access ready-made machine learning pipelines, application documentation and Tensorflow modules to help speed up the time it takes to get their AI projects off the ground.
Enterprises can also use the AI Hub as a private storage repository for their own machine learning and artificial intelligence resources, where they can then be shared with teams from other parts of the business.
In the beta release, Google said it plans to expand the type of assets made available through the AI Hub, including public contributions from third-party organisations and partners.
The resources made available in the alpha release, however, have all been developed in-house by Google developers, including members of the Google Cloud AI and Google Research teams.
The news also coincides with an expansion of Google’s Kubeflow portfolio, which is an open source initiative that aims to make the process of deploying and managing machine learning software stacks on Kubernetes containers easier.
As such, the firm is rolling out Kubeflow Pipelines that will enable users to create machine learning code bundles that can be packaged up and used by other users in the same organisation, while tapping into add-on services that can help them analyse and validate the models they are creating.
“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,” said Hussein Mehanna, engineering director of the Cloud machine learning platform at Google, in a supporting blog post.
“It also enables rapid and reliable experimentation, so users can try many machine learning techniques to identify what works best for their application.”
The blog post further reveals that Google now has more than 15,000 organisations paying to use its machine learning services across a range of industries, including manufacturing, e-commerce and healthcare.
“Our goal is to put AI in reach of all businesses, but doing that means lowering the barriers to entry. That’s why we build all our AI offerings with three ideas in mind: make them simple, so more enterprises can adopt them; make them useful to the widest range of organisations; and make them fast, so businesses can iterate and succeed more quickly,” said Mehanna.
This is important, he added, because of how few data scientists there are in the world to assist the developer community with helping enterprises realise their AI ambitions.
“Although there are approximately 20 million developers worldwide, there are only 2 million data scientists. They need tools that can help them scale their efforts, and organisations need more ways to take advantage of their work and make it accessible to their developers and engineers.”