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Unlock personalized experiences powered by AI using Amazon Personalize and Amazon OpenSearch Service

AWS Machine Learning

OpenSearch is a scalable, flexible, and extensible open source software suite for search, analytics, security monitoring, and observability applications, licensed under the Apache 2.0 You can also add data incrementally by importing records using the Amazon Personalize console or API. No ML expertise is required.

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

AWS Machine Learning

Today, we are excited to announce Promotions feature in Amazon Personalize that allows you to explicitly recommend specific items to your users based on rules that align with your business goals. You can use promotions in domain dataset groups and custom dataset groups ( User-Personalization and Similar-Items recipes).

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How Amp on Amazon used data to increase customer engagement, Part 2: Building a personalized show recommendation platform using Amazon SageMaker

AWS Machine Learning

Amp uses machine learning (ML) to provide personalized recommendations for live and upcoming Amp shows on the app’s home page. This is Part 2 of a series on using data analytics and ML for Amp and creating a personalized show recommendation list platform. Solution overview. Measuring the outcome.

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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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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 110
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Live Meeting Assistant with Amazon Transcribe, Amazon Bedrock, and Knowledge Bases for Amazon Bedrock

AWS Machine Learning

If you want to customize the settings later, for example to add your own AWS Lambda functions, use custom vocabularies and language models to improve accuracy, enable personally identifiable information (PII) redaction, and more, you can update the stack for these parameters. Choose Monitoring in the navigation pane to see API metrics.

APIs 106
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FMOps/LLMOps: Operationalize generative AI and differences with MLOps

AWS Machine Learning

These teams are as follows: Advanced analytics team (data lake and data mesh) – Data engineers are responsible for preparing and ingesting data from multiple sources, building ETL (extract, transform, and load) pipelines to curate and catalog the data, and prepare the necessary historical data for the ML use cases.