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Speed up your time series forecasting by up to 50 percent with Amazon SageMaker Canvas UI and AutoML APIs

AWS Machine Learning

As an example, time-series forecasting allows retailers to predict future sales demand and plan for inventory levels, logistics, and marketing campaigns. In this post, we describe the enhancements to the forecasting capabilities of SageMaker Canvas and guide you on using its user interface (UI) and AutoML APIs for time-series forecasting.

APIs 86
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5 Capabilities of Business Intelligence for Social Media Monitoring and Analytics

CSM Magazine

In this article, we’ll explore five key capabilities of BI that empower businesses to monitor social media conversations, analyze sentiment, conduct competitor analysis, create customized dashboards and reports, and integrate social media data with other sources for comprehensive analytics.

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Dynamic video content moderation and policy evaluation using AWS generative AI services

AWS Machine Learning

The frontend UI interacts with the extract microservice through a RESTful interface provided by Amazon API Gateway. It offers details of the extracted video information and includes a lightweight analytics UI for dynamic LLM analysis. The following screenshots show some examples.

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Intelligent document processing with AWS AI and Analytics services in the insurance industry: Part 2

AWS Machine Learning

We also look into how to further use the extracted structured information from claims data to get insights using AWS Analytics and visualization services. We highlight on how extracted structured data from IDP can help against fraudulent claims using AWS Analytics services. Amazon Redshift is another service in the Analytics stack.

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Boost agent productivity with Salesforce integration for Live Call Analytics

AWS Machine Learning

The Live Call Analytics with Agent Assist (LCA) open-source solution addresses these challenges by providing features such as AI-powered agent assistance, call transcription, call summarization, and much more. This function can implement custom logic that’s relevant to postprocessing, for example, updating the call summary to a CRM system.

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CBRE and AWS perform natural language queries of structured data using Amazon Bedrock

AWS Machine Learning

AWS Prototyping successfully delivered a scalable prototype, which solved CBRE’s business problem with a high accuracy rate (over 95%) and supported reuse of embeddings for similar NLQs, and an API gateway for integration into CBRE’s dashboards. The following diagram illustrates the web interface and API management layer.

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Use Kubernetes Operators for new inference capabilities in Amazon SageMaker that reduce LLM deployment costs by 50% on average

AWS Machine Learning

ACK is a framework for building Kubernetes custom controllers, where each controller communicates with an AWS service API. These controllers allow Kubernetes users to provision AWS resources like buckets, databases, or message queues simply by using the Kubernetes API. Release v1.2.9 services.k8s.aws/Bucket has been created.

APIs 91