Productized AI Models Drive ‘En Masse’ Intelligence

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Large Language Models,AI,SAS

I am a technology journalist with three decades of press experience. Primarily I work as a news analysis writer dedicated to a software application development ‘beat’. I have spent much of the last twenty years focusing on open source, analytics & data science, cloud computing, mobile devices & data management.

Productization is an affirmation. When any good or service evolves to a point where it can be productized and boxed, it denotes a certain solidity and branded consumability. Whether we’re talking about a packet of instant noodles, a pair of shoes or a standard haircut, when a commercial proposition is productized, it becomes easier for consumers to know its price, function and scope.

Quickly becoming popularized especially in the realm of generative AI, MaaS is argued to be more efficient, cost-effective and easier to scale with. Where AI models have been established and agreed to be without bias or risk of hallucination, this approach may also be said to be more robust. In this burgeoning space, MaaS providers offer documentation, tutorials and support, so developers can start integrating AI capabilities into software more quickly and competently.

"An area that is ripe for SAS is productizing models built on SAS' core assets, talent and IP from its wealth of experience working with customers to solve industry problems," offered Chandana Gopal, research director for future of intelligence, IDC.

 

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