Companies can make use of vector databases without needing a Ph.D. in AI

Companies can make use of vector databases without needing a Ph.D. in AI

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The last time Momento Inc. spoke with theCUBE, part of the conversation revolved around ChatGPT and how it was revolutionizing the world as we know it.

That hasn’t changed, and Daniela Miao (pictured, left), co-founder and chief technology officer of Momento, is even more excited these days. That’s because of some of the existing applications that are already out there, including GitHub Copilot and other kinds of artificial intelligence applications, according to Miao.

“The world is just so busy enabling AI in their applications, and I think now is the time to really care about time to market,” Miao said. “[How quickly can] each company launch their products as fast as possible and bring those benefits to their end users?”

Miao and Khawaja Shams (right), co-founder and chief executive officer of Momento, spoke with theCUBE industry analyst Lisa Martin during a CUBE Conversation at SiliconANGLE Media’s livestreaming studio. They discussed why Momento created its Vector Index and how it is different from existing solutions. (* Disclosure below.)

Aiming to improve developer productivity

A vector has long been a tool in a software engineer’s database. Vector databases have enabled them to speed up their application and enhance capabilities, such as recommendation systems inside their applications, according to Miao.

They are different compared to traditional databases in that they allow users to find things similar to what they’re looking for. That accelerates discovery, according to Shams.

Today, the world is busy enabling AI in their applications. That’s why Momento believes the race is on with time to market mattering more than ever. For that reason, the company started looking at what organizations are working on during their development cycles, with many building AI-enabled capabilities into their systems. Vector databases and indexes underpin a lot of that, according to Shams.

“The difference is, it’s really easy today to build an experiment with a vector index. And as soon as you go and try to operationalize it, take it to production, handle the scale, get the replication, get the availability, there’s a whole lot of knobs that start to appear,” he said. “You have to take this toy project, you have to deploy it in the cloud.”

Companies will need to understand how many replicas they need so they can ensure they are highly available. They must also understand capacity management to ensure it is scaling, as well as which indexing algorithms they’ll need to use, among other parameters, according to Shams.

“You’ve got to say how many indexing nodes, how many query nodes, how many storage nodes — all of that is completely surmountable,” he said. “But [any] cycles that people are spending on this are cycles that are not going towards innovation in their business. Our hope with this Momento Vector Index is to eliminate all of that.”

Momento Vector Index is completely transparent with two very simple APIs, according to Shams. The APIs “index a vector and then, given a vector, find me the top nearest neighbors for that given vector,” he said. “That’s it. All of that complexity that I just mentioned gets abstracted away. And that undifferentiated heavy lifting becomes Momento’s problem so that our customers can continue focusing on their business domain.”

Here’s the complete video interview with Daniela Miao and Khawaja Shams:

(* Disclosure: Momento Inc. sponsored this segment of theCUBE. Neither Momento nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE

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