In a few short years, the IT industry has moved from a paucity of artificial intelligence (AI) technologies to an embarrassment of riches. The challenge IT organizations now face is finding a way to incorporate them all.
To address that issue, Sinequa announced today it has integrated AI technologies from both Google and IBM into a Sinequa Logical Data Warehouse platform. In the case of Google, that means adding support for Google Vision and Translate AI software, as well as the Watson Alchemy application programming interfaces (APIs) for Image Recognition and Speech-to-Text services for translating languages.
Scott Parker, senior product marketing manager for Sinequa, says rather than implementing advanced AI technologies themselves, most organizations are going to access them via platforms such as Sinequa designed from the ground up to centralize access to multiple data sources. The Sinequa approach gives IT organizations the option of deploying that data warehouse on-premises or in a public cloud, says Parker.
“It can run wherever your data is,” says Parker.https://o1.qnsr.com/log/p.gif?;n=203;c=204663295;s=11915;x=7936;f=201904081034270;u=j;z=TIMESTAMP;a=20410779;e=i
Figuring out exactly where to put a data warehouse has become a more complicated decision in the age of the cloud. IT organizations have a vested interest in moving as little data as possible. If data is created in the cloud, it makes sense to apply the analytics there. If most of the data is created on-premises, it makes sense to deploy the data warehouse there. That data gravity effect not only influences the location of the data warehouse, but also where AI algorithms need to be applied.
Less clear, however, is the degree to which IT organizations will need to master all those algorithms themselves versus simply invoking them as a feature within a cognitive analytics application.