Remove AWS Remove Generative AI Remove Off-The-Shelf Remove Training
article thumbnail

AI adoption accelerates as enterprise PoCs show productivity gains

CIO

Some prospective projects require custom development using large language models (LLMs), but others simply require flipping a switch to turn on new AI capabilities in enterprise software. “AI We don’t want to just go off to the next shiny object,” she says. “We We want to maintain discipline and go deep.”

article thumbnail

Microsoft’s latest OpenAI investment opens way to new enterprise services

CIO

OpenAI has landed billions of dollars more funding from Microsoft to continue its development of generative artificial intelligence tools such as Dall-E 2 and ChatGPT. As a licensee of OpenAI’s software it will have access to new AI-based capabilities it can resell or build into its products.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Introducing the GenAI models you haven’t heard of yet

CIO

Ever since OpenAI’s ChatGPT set adoption records last winter, companies of all sizes have been trying to figure out how to put some of that sweet generative AI magic to use. Many, if not most, enterprises deploying generative AI are starting with OpenAI, typically via a private cloud on Microsoft Azure.

article thumbnail

Explore data with ease: Use SQL and Text-to-SQL in Amazon SageMaker Studio JupyterLab notebooks

AWS Machine Learning - AI

Amazon SageMaker Studio provides a fully managed solution for data scientists to interactively build, train, and deploy machine learning (ML) models. They then use SQL to explore, analyze, visualize, and integrate data from various sources before using it in their ML training and inference.

article thumbnail

Build a contextual chatbot for financial services using Amazon SageMaker JumpStart, Llama 2 and Amazon OpenSearch Serverless with Vector Engine

AWS Machine Learning - AI

The financial service (FinServ) industry has unique generative AI requirements related to domain-specific data, data security, regulatory controls, and industry compliance standards. RAG is a framework for improving the quality of text generation by combining an LLM with an information retrieval (IR) system.

article thumbnail

Deploy foundation models with Amazon SageMaker, iterate and monitor with TruEra

AWS Machine Learning - AI

These foundation models perform well with generative tasks, from crafting text and summaries, answering questions, to producing images and videos. Despite the great generalization capabilities of these models, there are often use cases where these models have to be adapted to new tasks or domains.