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How Amp on Amazon used data to increase customer engagement, Part 1: Building a data analytics platform

AWS Machine Learning

Amp wanted a scalable data and analytics platform to enable easy access to data and perform machine leaning (ML) experiments for live audio transcription, content moderation, feature engineering, and a personal show recommendation service, and to inspect or measure business KPIs and metrics. Business intelligence (BI) and analytics.

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Live Meeting Assistant with Amazon Transcribe, Amazon Bedrock, and Knowledge Bases for Amazon Bedrock

AWS Machine Learning

When the user is authenticated, the web application establishes a secure GraphQL connection to the AWS AppSync API, and subscribes to receive real-time events such as new calls and call status changes for the meetings list page, and new or updated transcription segments and computed analytics for the meeting details page.

APIs 107
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Use RAG for drug discovery with Knowledge Bases for Amazon Bedrock

AWS Machine Learning

The Retrieve and RetrieveAndGenerate APIs allow your applications to directly query the index using a unified and standard syntax without having to learn separate APIs for each different vector database, reducing the need to write custom index queries against your vector store.

APIs 111
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What Is CPaaS? Communication Platform as a Service Explained

JustCall

It is a cloud-based model of delivery that lets the user add video, messaging, and voice features to their existing software using the APIs. It uses the communication Application Programming Interface (APIs) to connect with existing apps and software. APIs these two servers to interact efficiently. Call Analytics.

APIs 52
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Customize your recommendations by promoting specific items using business rules with Amazon Personalize

AWS Machine Learning

You can get started without any prior machine learning (ML) experience, using APIs to easily build sophisticated personalization capabilities in a few clicks. You can use the Amazon Personalize console or API to create a filter with your logic using the Amazon Personalize DSL (domain-specific language). It only takes a few minutes.

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Simplify iterative machine learning model development by adding features to existing feature groups in Amazon SageMaker Feature Store

AWS Machine Learning

In this post, we demonstrate how to add features to a feature group using the newly released UpdateFeatureGroup API. To update the feature group to add a new feature, we use the new Amazon SageMaker UpdateFeatureGroup API. We ingest the DataFrame into the feature group using the SageMaker SDK FeatureGroup.ingest API.

APIs 83
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How to Migrate from an On-premise Call Center Solution to a Cloud one

3CLogic

If an enterprise has already invested in an on-premise telephony infrastructure but is starting to entertain the merits of migrating to the cloud, a Connector can be a good first step.