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Accelerating time-to-insight with MongoDB time series collections and Amazon SageMaker Canvas

AWS Machine Learning

Industries such as Finance, Retail, Supply Chain Management, and Logistics face the risk of missed opportunities, increased costs, inefficient resource allocation, and the inability to meet customer expectations. Solution overview Users persist their transactional time series data in MongoDB Atlas.

Finance 115
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Access Snowflake data using OAuth-based authentication in Amazon SageMaker Data Wrangler

AWS Machine Learning

Access and permissions to configure IDP to register Data Wrangler application and set up the authorization server or API. For data scientist: An S3 bucket that Data Wrangler can use to output transformed data. His knowledge ranges from application architecture to big data, analytics, and machine learning.

APIs 79
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Intelligently search Adobe Experience Manager content using Amazon Kendra

AWS Machine Learning

Perform intelligent search with Amazon Kendra Before you try searching on the Amazon Kendra console or using the API, make sure that the data source sync is complete. To check, view the data sources and verify if the last sync was successful. She is passionate about designing big data workloads cloud-natively.

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Call Center Analytics: How to Analyze Call Center Data

Balto

But modern analytics goes beyond basic metricsit leverages technologies like call center data science, machine learning models, and big data to provide deeper insights. Predictive Analytics: Uses historical data to forecast future events like call volumes or customer churn. What is contact center big data analytics?

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MLOps foundation roadmap for enterprises with Amazon SageMaker

AWS Machine Learning

This might be a triggering mechanism via Amazon EventBridge , Amazon API Gateway , AWS Lambda functions, or SageMaker Pipelines. In addition to the model endpoint, the CI/CD also tests the triggering infrastructure, such as EventBridge, Lambda functions, or API Gateway. Data lake and MLOps integration.

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A review of purpose-built accelerators for financial services

AWS Machine Learning

In terms of resulting speedups, the approximate order is programming hardware, then programming against PBA APIs, then programming in an unmanaged language such as C++, then a managed language such as Python. SIMD describes computers with multiple processing elements that perform the same operation on multiple data points simultaneously.

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Call Centers Go Mobile to Satisfy Higher Customer Service Expectations

CSM Magazine

As in other verticals such as retail, health and finance, the consumer is now at the center of operational design and customer satisfaction is the new and key-performance index. These business models need to be revisited. The challenge for many providers is executing on this vision.