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Performance Management Bridges the Divide Between Big Data and Big Knowledge

Aspect

There is information everywhere: in your ACD , WFM, CRM, quality management, recording, surveys, speech analytics and self-service systems. That’s exactly what Performance Management (PM) does in the contact center, but it’s a long road from information to knowledge.

Big data 102
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Amazon SageMaker Feature Store now supports cross-account sharing, discovery, and access

AWS Machine Learning

Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, share, and manage features for machine learning (ML) models. SageMaker Feature Store now makes it effortless to share, discover, and access feature groups across AWS accounts. Features are inputs to ML models used during training and inference.

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Set up cross-account Amazon S3 access for Amazon SageMaker notebooks in VPC-only mode using Amazon S3 Access Points

AWS Machine Learning

To develop models for such use cases, data scientists need access to various datasets like credit decision engines, customer transactions, risk appetite, and stress testing. Managing appropriate access control for these datasets among the data scientists working on them is crucial to meet stringent compliance and regulatory requirements.

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Use Amazon SageMaker pipeline sharing to view or manage pipelines across AWS accounts

AWS Machine Learning

On August 9, 2022, we announced the general availability of cross-account sharing of Amazon SageMaker Pipelines entities. You can now use cross-account support for Amazon SageMaker Pipelines to share pipeline entities across AWS accounts and access shared pipelines directly through Amazon SageMaker API calls. Solution overview.

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Top 5 Customer Service & CX Articles for Week of April 29, 2024

ShepHyken

For context, these are the customers who continue to buy from you over and over again, and should account for the majority of your total sales. This article includes five ways to manage the VIP experience, and of course, there’s technology, personalization, and more. Years ago, the term “Big Data” became popular.

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Accelerate data preparation for ML in Amazon SageMaker Canvas

AWS Machine Learning

Data preparation is a crucial step in any machine learning (ML) workflow, yet it often involves tedious and time-consuming tasks. Amazon SageMaker Canvas now supports comprehensive data preparation capabilities powered by Amazon SageMaker Data Wrangler. To import data from Snowflake, follow steps from Set up OAuth for Snowflake.

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Simplify data prep for generative AI with Amazon SageMaker Data Wrangler

AWS Machine Learning

However, these models require massive amounts of clean, structured training data to reach their full potential. Most real-world data exists in unstructured formats like PDFs, which requires preprocessing before it can be used effectively. According to IDC , unstructured data accounts for over 80% of all business data today.