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Accelerate your ML lifecycle using the new and improved Amazon SageMaker Python SDK – Part 2: ModelBuilder

Accelerate your ML lifecycle using the new and improved Amazon SageMaker Python SDK – Part 2: ModelBuilder

In Part 1 of this series, we introduced the newly launched ModelTrainer class on the Amazon SageMaker Python SDK and its benefits, and showed you how to fine-tune a Meta Llama 3.1 8B model on a custom dataset. In this post, we look at the enhancements to the ModelBuilder class, which lets you seamlessly deploy …

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Accelerate your ML lifecycle using the new and improved Amazon SageMaker Python SDK – Part 1: ModelTrainer

Accelerate your ML lifecycle using the new and improved Amazon SageMaker Python SDK – Part 1: ModelTrainer

Amazon SageMaker has redesigned its Python SDK to provide a unified object-oriented interface that makes it straightforward to interact with SageMaker services. The new SDK is designed with a tiered user experience in mind, where the new lower-level SDK (SageMaker Core) provides access to full breadth of SageMaker features and configurations, allowing for greater flexibility …

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Amazon Q Apps supports customization and governance of generative AI-powered apps

Amazon Q Apps supports customization and governance of generative AI-powered apps

We are excited to announce new features that allow creation of more powerful apps, while giving more governance control using Amazon Q Apps, a capability within Amazon Q Business that allows you to create generative AI-powered apps based on your organization’s data. These features enhance app customization options that let business users tailor solutions to …

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Answer questions from tables embedded in documents with Amazon Q Business

Answer questions from tables embedded in documents with Amazon Q Business

Amazon Q Business is a generative AI-powered assistant that can answer questions, provide summaries, generate content, and securely complete tasks based on data and information in your enterprise systems. A large portion of that information is found in text narratives stored in various document formats such as PDFs, Word files, and HTML pages. Some information …

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How AWS sales uses Amazon Q Business for customer engagement

How AWS sales uses Amazon Q Business for customer engagement

Earlier this year, we published the first in a series of posts about how AWS is transforming our seller and customer journeys using generative AI. In addition to planning considerations when building an AI application from the ground up, it focused on our Account Summaries use case, which allows account teams to quickly understand the …

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Discover insights from your Amazon Aurora PostgreSQL database using the Amazon Q Business connector

Discover insights from your Amazon Aurora PostgreSQL database using the Amazon Q Business connector

Amazon Aurora PostgreSQL-Compatible Edition is a fully managed, PostgreSQL-compatible, ACID-aligned relational database engine that combines the speed, reliability, and manageability of Amazon Aurora with the simplicity and cost-effectiveness of open source databases. Aurora PostgreSQL-Compatible is a drop-in replacement for PostgreSQL and makes it simple and cost-effective to set up, operate, and scale your new and …

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How Tealium built a chatbot evaluation platform with Ragas and Auto-Instruct using AWS generative AI services

How Tealium built a chatbot evaluation platform with Ragas and Auto-Instruct using AWS generative AI services

This post was co-written with Varun Kumar from Tealium Retrieval Augmented Generation (RAG) pipelines are popular for generating domain-specific outputs based on external data that’s fed in as part of the context. However, there are challenges with evaluating and improving such systems. Two open-source libraries, Ragas (a library for RAG evaluation) and Auto-Instruct, used Amazon …

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