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Enable fully homomorphic encryption with Amazon SageMaker endpoints for secure, real-time inferencing

AWS Machine Learning

Homomorphic encryption is a new approach to encryption that allows computations and analytical functions to be run on encrypted data, without first having to decrypt it, in order to preserve privacy in cases where you have a policy that states data should never be decrypted. The following figure shows both versions of these patterns.

Scripts 97
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The Future of Debt Collection Agencies: Contact Center Technology and Customer-Centric Strategies

NobelBiz

Advanced analytics and machine learning algorithms are also being integrated into call center operations to predict debtor behavior and optimize contact strategies. Call centers are equipped with tools that allow agents to quickly access a debtor’s full account information, ensuring that every interaction is informed and constructive.

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How Patsnap used GPT-2 inference on Amazon SageMaker with low latency and cost

AWS Machine Learning

A recent initiative is to simplify the difficulty of constructing search expressions by autofilling patent search queries using state-of-the-art text generation models. In this section, we show how to build your own container, deploy your own GPT-2 model, and test with the SageMaker endpoint API. Specifically, Dockerfile and build.sh

APIs 66
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Supercharge your AI team with Amazon SageMaker Studio: A comprehensive view of Deutsche Bahn’s AI platform transformation

AWS Machine Learning

At Deutsche Bahn, a dedicated AI platform team manages and operates the SageMaker Studio platform, and multiple data analytics teams within the organization use the platform to develop, train, and run various analytics and ML activities.

APIs 101
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Boosting RAG-based intelligent document assistants using entity extraction, SQL querying, and agents with Amazon Bedrock

AWS Machine Learning

Another driver behind RAG’s popularity is its ease of implementation and the existence of mature vector search solutions, such as those offered by Amazon Kendra (see Amazon Kendra launches Retrieval API ) and Amazon OpenSearch Service (see k-Nearest Neighbor (k-NN) search in Amazon OpenSearch Service ), among others.

Analytics 104
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How Marubeni is optimizing market decisions using AWS machine learning and analytics

AWS Machine Learning

In this post, you will learn how Marubeni is optimizing market decisions by using the broad set of AWS analytics and ML services, to build a robust and cost-effective Power Bid Optimization solution. The data collection functions call their respective source API and retrieve data for the past hour.

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How Q4 Inc. used Amazon Bedrock, RAG, and SQLDatabaseChain to address numerical and structured dataset challenges building their Q&A chatbot

AWS Machine Learning

The Q4 Platform facilitates interactions across the capital markets through IR website products, virtual events solutions, engagement analytics, investor relations Customer Relationship Management (CRM), shareholder and market analysis, surveillance, and ESG tools. LangChain supports Amazon Bedrock as a multi-foundation model API.

Chatbots 100