IBM is leveraging Kubernetes to enable its Watson AI to run on public clouds AWS, Google, and Microsoft Azure. The move signals a shift in strategy for IBM.
Cloud computing has made a lot of technology more accessible, and artificial intelligence and its underlying technologies are no exception. If you want more organizations to be able to use your technology, then make it possible for them to use it on one of the big public cloud providers -- Microsoft Azure, Google Cloud Platform, and Amazon Web Services (AWS).
Indeed, many organizations are now using the AI services that are available and have been built on those public cloud platforms -- AWS Rekognition, for instance.
In an effort to broaden the distribution of its flagship artificial intelligence technology, IBM this week announced that it is making IBM Watson portable across all these public cloud services. The company unveiled the strategy this week at the IBM Think 2019 event in San Francisco.
"Clients want the ability to bring artificial intelligence to their data wherever it resides," general manager for IBM data and AI, Rob Thomas, told InformationWeek in an interview.
The move marks a new era for the Watson AI, which was previously only available on IBM's public cloud and more recently in Kubernetes containers in on-premises private clouds. IBM rolled out the IBM Cloud Private strategy a year ago to enable enterprises to run IBM services in their own on-premises private clouds using Kubernetes.
Making those containers with IBM services able to run on the big three public cloud providers was a logical next step. Thomas hinted at such a move last year during the IBM Cloud Private roll out, saying that it was theoretically possible, pending testing.
Using Watson in a container on AWS, Google Cloud Platform, or Microsoft Azure may make sense for some enterprises that want to invest in AI but don't want to be locked into the AI services that have been developed and are provided by those three public cloud platform leaders. For instance, if you use the AWS native AI services, you can't easily migrate your work to Microsoft Azure and that company's native AI services. Deploying your AI in a container to one of these public clouds provides you with more portability.
