Google has unveiled a range of tools aimed at democratising access to machine learning and putting the technology within “reach of all businesses”.
It comes just months after the US tech giant unveiled a suite of products designed to enable developers with no machine learning experience to build ML models.
The two initiatives form part of a drive by the tech giant to distinguish its cloud computing services as AI-friendly, in a bid to challenge its more established cloud rivals, Amazon Web Services and Microsoft Azure.
The first of Google’s two new products is called AI Hub and aims to provide a “one-stop destination for plug-and-play ML content, including pipelines, Jupyter notebooks, TensorFlow modules, and more”. It makes Google-developed machine learning tools available to all businesses for the first time.
Developers, data scientists and engineers can also use the hub to securely upload and share their own machine learning tools in private.
The second tool is Kubeflow Pipeline, a new component of an open source project founded by Google that lets developers reuse ML code across different parts of their organisation. The fact that the tools enables users to repurpose their workflows makes it a “no lock-in solution”, according to Google.
“It’s not enough to provide a place where organizations can discover, share and reuse ML resources, they also need a way to build and package them so that they’re as useful as possible to the broadest range of internal users,” said Google Cloud’s ML platform engineer director Hussein Mehanna. “That’s why we’re introducing Kubeflow Pipelines.”
Nicholas McQuire, head of AI research at CCS Insight, described the products as important and differentiating.
“Customer fear of being locked in by the cloud providers is reaching an all-time high and this has been a key barrier for AI adoption,” he added. “Meanwhile, hybrid cloud and open source technologies like Kubernetes, which Google pioneered, have become very popular so Kubeflow Pipelines addresses many AI requirements in a single stroke.
“Additionally, AI Hub enables Google to build on the already extensive community around its AI content and tools with Kaggle and TensorFlow, for example, for enterprises to improve their internal collaboration on machine learning projects.
“Above all, the introduction of Kubeflow Pipelines and AI Hub reinforce Google’s large scale efforts in 2018 to invest in artificial intelligence. As the underdog in the cloud wars against Amazon and Microsoft, AI has become its most important lever in enticing customers to its cloud services.”