Remove Business Intelligence Remove Metadata Remove NLP
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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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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. These insights can include: Potential adverse event detection and reporting.

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Exploring the AI and data capabilities of watsonx

IBM Journey to AI blog

These encoder-only architecture models are fast and effective for many enterprise NLP tasks, such as classifying customer feedback and extracting information from large documents. ” Vitaly Tsivin, EVP Business Intelligence at AMC Networks. To bridge the tuning gap, watsonx.ai Later this year, it will leverage watsonx.ai

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Dr. Mike Flaxman, VP or Product Management at HEAVY.AI – Interview Series

Unite.AI

IQ is about making data exploration and visualization as intuitive as possible by using natural language processing (NLP). Traditional business intelligence tools often struggle with the volume and speed of this data. What measures are in place to prevent metadata leakage when using HeavyIQ? How does HEAVY.AI

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

AWS Machine Learning Blog

Each request/response interaction is facilitated by the AWS SDK and sends network traffic to Amazon Lex (the NLP component of the bot). Metadata about the request/response pairings are logged to Amazon CloudWatch. The CloudWatch log group is configured with a subscription filter that sends logs into Amazon OpenSearch Service.