Remove Business Intelligence Remove Categorization Remove Metadata
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Asure’s approach to enhancing their call center experience using generative AI and Amazon Q in Quicksight

AWS Machine Learning Blog

Asure chose this approach because it provided in-depth consumer analytics, categorized call transcripts around common themes, and empowered contact center leaders to use natural language to answer queries. The original PCA post linked previously shows how Amazon Transcribe and Amazon Comprehend are used in the metadata generation pipeline.

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

AWS Machine Learning Blog

Manually analyzing and categorizing large volumes of unstructured data, such as reviews, comments, and emails, is a time-consuming process prone to inconsistencies and subjectivity. We provide a prompt example for feedback categorization. Extracting valuable insights from customer feedback presents several significant challenges.

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

AWS Machine Learning Blog

After a few minutes, a transcript is produced with Amazon Transcribe Call Analytics and saved to another S3 bucket for processing by other business intelligence (BI) tools. PCA also offers a web-based user interface that allows customers to browse call transcripts.

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Publish predictive dashboards in Amazon QuickSight using ML predictions from Amazon SageMaker Canvas

AWS Machine Learning Blog

Business analysts play a pivotal role in facilitating data-driven business decisions through activities such as the visualization of business metrics and the prediction of future events. You can add metadata to the policy by attaching tags as key-value pairs, then choose Next: Review. Choose Next: Tags.

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A brief history of Data Engineering: From IDS to Real-Time streaming

Artificial Corner

Data warehouses were designed to support business intelligence activities, providing a centralized data source for reporting and analysis. This multidimensional analysis capability makes OLAP ideal for business intelligence applications, where users must analyze data from various perspectives.

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Quantization Aware Training in PyTorch

Bugra Akyildiz

The resulting learned embeddings and associated metadata as features is then inputted to a survival model for predicting 10-year incidence of major adverse cardiac events. Evidence is an open-source, code-based alternative to drag-and-drop business intelligence tools. It has a great project page as well.

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Governing ML lifecycle at scale: Best practices to set up cost and usage visibility of ML workloads in multi-account environments

AWS Machine Learning Blog

Technical tags – These provide metadata about resources. The AWS reserved prefix aws: tags provide additional metadata tracked by AWS. Business tags – These represent business-related attributes, not technical metadata, such as cost centers, business lines, and products.

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