Oracle Ships Generative AI Cloud Service, Beta for 'AI Agents'
Oracle announced the general availability of a new generative AI cloud service along with a beta offering for a related AI Agents service.
Oracle Cloud Infrastructure (OCI) Generative AI
is described as a fully managed service that provides an API to seamlessly integrate large language models (LLMs) into projects for writing assistance, summarization, analysis and chat.
Specific use cases include:
- Customer operations: Automating customer service based on a customer's product suite, experience, and language.
- Marketing: Mass personalization of search, outreach, and customer nurture based on buyer profile and purchase history.
- Sales: Creating virtual sales representatives that guide prospects through offerings during a business transaction
- Risk and legal: Accelerating contract writing and drafting based on existing best practices, with multilingual support.
- Strategy and finance: At scale monitoring of competitors and updates from customers, across public and private sources.
The cloud computing provider also is getting in on one of the latest AI fads, "agents" that are customized and tailored to work with company data, acting like specialized mini-ChatGPT chatbots specific to an organization's setup. Oracle's take on agents is called OCI Generative AI Agents service, a beta release.
"Agents translate user queries into tasks that Generative AI components (search tool, document corpus, LLM, response generation, etc.) perform to answer the queries," Oracle said in a Jan 23 post. "The first in a series of OCI Generative AI Agents is a retrieval augmented generation (RAG) agent that complements the general knowledge of LLMs with internal data using OCI OpenSearch to provide contextually relevant answers. Users can now transparently access diverse enterprise data sets through natural language without the need for specialist skills or to know the data's format or storage location."
The company also touted another new AI offering in the works, OCI Data Science AI Quick Actions, described as a no-code feature of the company's OCI Data Science service that eases access to a wide range of open source LLMs, including offerings from Meta, Mistral AI and more.
"OCI Data Science Quick Actions provides access to curated models that users can fine tune, evaluate, and deploy with their data, but also encompasses a comprehensive ecosystem with user-friendly workflows, integrated telemetry and visualizations, and simplified execution processes," said Oracle, which listed the following features expected to be ready for use when the service reaches general availability:
- Verification and environment pre-check
- A list of curated, pre-tested models -- including LLMs
- Curated deployment options
- The ability to search and filter models and the flexibility to select the model that best suits customer needs
- A few-clicks interface for executing fine-tuning tasks, with guided steps
- Monitoring fine tuning tasks can be executed directly from the console
- A playground feature for quickly testing deployed models
Oracle, like other cloud providers, is going all in on AI in order to gain market share. AI was widely credited with boosting the most recent quarterly financial results from the "Big 3" cloud giants and, according to brand-new Synergy Research Group data, is propelling cloud spending to new heights. As the following chart indicates, however, Oracle is at the bottom of that pack.
Trying to catch up, the company summarized upcoming AI enhancements to its various services thusly:
- Oracle Digital Assistant: Now allows you to incorporate data from LLMs along with your enterprise data into chat experiences. Oracle Digital Assistant combines database information, enterprise knowledge documents, and generative AI to enable natural language conversations with minimal effort. Developers can use generative AI capabilities to build digital assistants more quickly and efficiently.
- OCI Language: Adds healthcare insights with natural language processing. This new feature of the OCI Language service helps better process language in the medical domain. New models will help recognize medical terms, relationships and entities in records such as clinical trials notes, patient progress notes, and electronic health records.
- Document Translation Experience: Adds a new file-based document translation feature to support a wide range of formats -- Word, .PPT, HTML, JSON, and Excel -- helping to ensure that content remains intact and translated across diverse file types.
- OCI Vision: Adds Facial Detection, now with the ability to recognize faces and facial features in images, and expanding the number of OCI Vision use cases. Additionally, Vision will support file-based video processing.
- OCI Speech: Adds diarization, allowing the service to embed speaker information into transcribed sections of audio. Diarization makes OCI Speech a valuable tool for organizing, analyzing, and extracting meaningful information from spoken interactions. Additionally, Speech will support real-time transcription and usage of open-source models such as Whisper.
- OCI Document Understanding: Support for over 300 additional languages and model improvements.
- OCI Data Science: Adds Feature Store, a central repository used to manage features developed by data science teams. Feature Store provides a cohesive framework where features are meticulously documented, shared, stored, and served in a streamlined manner. Also, a new Forecasting operator is available so customers can leverage Oracle's proprietary deep learning models through OCI Data Science with a no-code operator.
"With today's news, Oracle is bringing generative AI to customer workloads and their data -- not asking customers to move their data to a separate vector database," the company quoted IDC exec Ritu Jyoti as saying. "With a common architecture for generative AI that is being integrated across the Oracle ecosystem from its Autonomous Database to Fusion SaaS applications, Oracle is bringing generative AI to where exabytes of customer data already reside, both in cloud data centers and on-premises environments. This greatly simplifies the process for organizations to deploy generative AI with their existing business operations."
David Ramel is an editor and writer for Converge360.