Remove Business Intelligence Remove Metadata Remove Natural Language Processing
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How to use foundation models and trusted governance to manage AI workflow risk

IBM Journey to AI blog

It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. A data store lets a business connect existing data with new data and discover new insights with real-time analytics and business intelligence. Track models and drive transparent processes.

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Five benefits of a data catalog

IBM Journey to AI blog

It uses metadata and data management tools to organize all data assets within your organization. It synthesizes the information across your data ecosystem—from data lakes, data warehouses, and other data repositories—to empower authorized users to search for and access business-ready data for their projects and initiatives.

Metadata 130
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Exploring the AI and data capabilities of watsonx

IBM Journey to AI blog

This allows users to accomplish different Natural Language Processing (NLP) functional tasks and take advantage of IBM vetted pre-trained open-source foundation models. Encoder-decoder and decoder-only large language models are available in the Prompt Lab today. To bridge the tuning gap, watsonx.ai

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Revolutionizing clinical trials with the power of voice and AI

AWS Machine Learning Blog

Intelligent insights and recommendations Using its large knowledge base and advanced natural language processing (NLP) capabilities, the LLM provides intelligent insights and recommendations based on the analyzed patient-physician interaction. You can download a sample file and review the contents.

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Build an automated insight extraction framework for customer feedback analysis with Amazon Bedrock and Amazon QuickSight

AWS Machine Learning Blog

Businesses can use LLMs to gain valuable insights, streamline processes, and deliver enhanced customer experiences. In addition, the generative business intelligence (BI) capabilities of QuickSight allow you to ask questions about customer feedback using natural language, without the need to write SQL queries or learn a BI tool.

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Principal Financial Group uses AWS Post Call Analytics solution to extract omnichannel customer insights

AWS Machine Learning Blog

As a first step, they wanted to transcribe voice calls and analyze those interactions to determine primary call drivers, including issues, topics, sentiment, average handle time (AHT) breakdowns, and develop additional natural language processing (NLP)-based analytics.

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Accenture creates a Knowledge Assist solution using generative AI services on AWS

AWS Machine Learning Blog

Online reporting The online reporting process consists of the following steps: End-users interact with the chatbot via a CloudFront CDN front-end layer. Metadata about the request/response pairings are logged to Amazon CloudWatch.