With the AI revolution in full swing, there’s a growing need for a simpler way to understand and deploy the smart technology so businesses can see its full potential.
And, it’s not just big organisations; it’s small and medium-sized businesses from all industries looking to get the most out of machine learning and data. To help manage these dauntingly complex technologies, Google Cloud is launching an AI Hub and Kubeflow Pipelines for businesses.
It’s as complex as it sounds and proof of Google Cloud’s point. For every business to fully understand AI and machine learning, they need a little help and guidance which Google Cloud is packaging as a set of building blocks.
However, the cloud giant’s new chief has more of a warning tone for businesses adopting these new technologies. Speaking to MIT Review, Andrew Moore laid bare the reality of embedded AI and machine learning into a business.
«It’s like electrification,» he said. «And it took about two or three decades for electrification to pretty much change the way the world was. Sometimes I meet very senior people with big responsibilities who have been led to believe that artificial intelligence is some kind of ‘magic dust’ that you sprinkle on an organisation and it just gets smarter. In fact, implementing artificial intelligence successfully is a slog.
«When people come in and say ‘How do I actually implement this artificial-intelligence project?’ we immediately start breaking the problems down in our brains into the traditional components of AI-perception, decision making, action and map those into different parts of the business. One of the things Google Cloud has in place is these building blocks that you can slot together.»
The AI Hub is described as a «one-stop destination for plug-and-play machine learning content» and includes TensorFlow modules. This, Google Cloud says, makes it easier for businesses to reuses pipelines and quickly deploy them to production in the Google Cloud Platform in a few simple steps.
The pipelines themselves are also a new component of Kubeflow, which is an open source project that packages ML code. It provides a workbench to compose, deploy and manage reusable ML workflows, making a «no lock-in hybrid solution» according to Google Cloud.
The introduction of Kubeflow Pipelines and the AI Hub reinforces Google’s large-scale efforts in 2018 to invest in artificial intelligence. As the bronze medalist in the cloud wars against Amazon and Microsoft, AI has become its most important product to entice customers to its cloud services.
«These are important, differentiating moves in artificial intelligence from Google,» states Nicholas McQuire, head of enterprise and artificial intelligence research at CCS Insight.
«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. 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.