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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 108
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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.

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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. Under Available OAuth Scopes , choose Manage user data via APIs (api). Choose Save. Verify your identity if prompted.

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Architect defense-in-depth security for generative AI applications using the OWASP Top 10 for LLMs

AWS Machine Learning

Consider inserting AWS Web Application Firewall (AWS WAF) in front to protect web applications and APIs from malicious bots , SQL injection attacks, cross-site scripting (XSS), and account takeovers with Fraud Control. Use package mirrors, scanning, and additional inspections as relevant to your organizations security goals.

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How Veritone uses Amazon Bedrock, Amazon Rekognition, Amazon Transcribe, and information retrieval to update their video search pipeline

AWS Machine Learning

Founded in 2014, Veritone empowers people with AI-powered software and solutions for various applications, including media processing, analytics, advertising, and more. Growing in the media and entertainment space, Veritone solves media management, broadcast content, and ad tracking issues.

APIs 100
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Incorporate offline and online human – machine workflows into your generative AI applications on AWS

AWS Machine Learning

An important aspect of developing effective generative AI application is Reinforcement Learning from Human Feedback (RLHF). RLHF is a technique that combines rewards and comparisons, with human feedback to pre-train or fine-tune a machine learning (ML) model. Therefore, you can use either or both based on your needs.