When Google LLC announced latest generative AI features for its cloud platform in April, the corporate provided users with a capability to make use of technology from Elasticsearch Inc. to leverage cross-network data for model training.
The announcement highlighted how Google Cloud has been working with its partners to supply a one-stop shop for enterprise generative artificial intelligence development.
“Every customer has a … data problem,” said Ken Exner (pictured, right), chief product officer at Elastic. The mixture of Google’s data processing and storage with Elastic’s search, “analytics and AI capabilities create a robust technique to sift through all that data to get to insights and to get to things that they will [take] motion on,” Exner further explained.
Exner spoke with theCUBE Research’s John Furrier for the Google Cloud Marketplace Marvels interview series, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. He was joined by Stephen Orban (left), vice chairman of migrations, ISVs and marketplace at Google Cloud, and so they discussed how the partnership is providing customers with revolutionary ways to deploy and manage generative AI. (* Disclosure below.)
Constructing advantage through use of personal data for generative AI
Elastic’s solutions bring together intelligent content search, IT observability and security analytics to assist customers access critical data for enterprise applications. With increased adoption of generative AI, businesses are in search of ways to achieve a competitive edge through using private data.
“While you discuss data and what the impact of that’s in customers’ generative AI pursuits, the models that we make available are great but they’re currently trained on publicly available data,” Orban noted. “Customers who’re doing unique things with their generative AI capabilities … they must be either tuned or augmented using, let’s say, retrieval augmented generation with data that they own in their very own private corpuses. Oftentimes, customers are already using Elastic because the storage layer for that corpus of knowledge.”
A desire to access private and non-private data stores represents a change in how customers have viewed data previously, in keeping with Exner.
“Certainly one of the things we’ve been seeing on this industry for some time is how do you take care of all this data?” he said. “Typically, people have been trying to scale back the quantity of knowledge that they’ve. What’s different as of late is that it’s OK to have all that data should you can search through it and parse it after which pass it to an LLM and have an LLM assist you to get answers out of it. Actually, the more data, the higher.”
Unique capabilities drive expanding use cases
Having more data has placed greater emphasis on automation and speed. Firms need to implement generative AI for a wide selection of uses which, in turn, is driving Google Cloud’s partnership with Elastic.
“Generative AI goes to make way for therefore many use cases we haven’t even considered yet, but additionally automate numerous existing things today, whether or not it’s customer support or summarizing documents or generating code or images or marketing campaigns,” Orban said. “The unique set of capabilities that Elastic has built with us over time … really position us well to assist customers, not only discover those use cases but implement them in a short time.”
The implementation of use cases requires a capability to leverage inferencing tools provided by Google Cloud on the Elastic platform. These include Vertex AI, which is used to serve large language models, and Gemini Pro, a part of Google Cloud’s family of generative AI offerings.
“Within the inference a part of this, we not only support our own inference model, we support Vertex AI embeddings creation services so you could do this directly from the [open] inference API in Elasticsearch, and we support passing context to an LLM at the tip of that workflow, on this case Gemini Pro,” Exner said. “You should utilize Elastic … with the very best generative AI at Google to create an entire workflow for grounding and constructing an LLM-based application on top of Google and Elastic.”
This mix has been helpful for patrons in search of support in areas akin to security, in keeping with Exner.
“You ought to have anomaly detection that runs to seek out things you could’t necessarily predefine as an alert,” he explained. “The mixture of predefined alerts and anomaly detection based on generative AI and machine learning models will allow the operations analysts to get the very best of each worlds. Due to the integrations that we’ve got with Gemini and the LLMs we will start automating the remediation workflow.”
Google Cloud is working with Elastic to construct latest features, including using Gemini Code Assist, a service inside Google Cloud Console where customers can ask questions and receive natural language responses.
“There’s more that we will do specifically to make sure that that when a customer is beginning to construct or train a model, they may discover an Elastic data source that they’re already running in GCP without necessarily having to go away the experience that they’re in,” Orban said. “We’ve began that integration already by Elastic working with us and training our Gemini Code Assist with Elastic specific data. There’s rather a lot more that we will and might be doing as the long run unfolds.”
Here’s the whole video interview, a part of SiliconANGLE’s and theCUBE Research’s coverage of the Google Cloud Marketplace Marvels interview series:
(* Disclosure: TheCUBE is a paid media partner for the Google Cloud Marketplace Marvels special interview series. Neither Google LLC, the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)
Photo: SiliconANGLE
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