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Optimize cloud costs and accelerate innovation with data and AI

The data of a startup offers immense value. It can be used to drive innovation, uncover new revenue streams, and gain a competitive advantage. However, storing, processing, and analyzing large amounts of data requires significant resources, such as hardware, software, and personnel. Now, the advent of generative AI adds another layer of complexity and cost that founders must consider.

Read the new ebook, "Scaling Smart: Leveraging AWS for cost-effective growth" to learn how you can create a cost-effective data strategy on AWS that delivers quantifiable value and supports business growth and innovation.

These are also closely related to: "Use LangChain and vector search on Amazon DocumentDB to build a generative AI chatbot"

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    Graph databases, like Amazon Neptune, play a critical role in building powerful generative AI (GenAI) solutions. Graphs are known for their efficient handling of complex and interconnected data and are particularly well suited to the demands of GenAI applications, which require rapid processing of relationships and patterns within large data sets.

    They further facilitate advanced data modeling, real-time data processing, and scalability—features essential for the dynamic and context-rich tasks handled by GenAI. With technologies like GraphRAG, graphs enable the development of more nuanced and context-aware GenAI applications by enabling the sophisticated management of semantic relationships within data.

    This capability is crucial for applications such as personalized recommendation engines, sophisticated content generation, knowledge graphs, and dynamic interaction systems, all of which rely on deep contextual understanding to function effectively.

    This white paper aims to guide customers on how they improve the quality of GenAI applications using graphs to significantly enhance the capabilities of GenAI applications, ultimately driving competitive advantages and innovation.

  • Generative AI + Healthcare guidebook

    Amazon Web Services (AWS) helps healthcare systems transform the way they care for patients with solutions supporting storing, sharing, and analyzing medical data. To improve patient outcomes, healthcare organizations are turning to generative artificial intelligence (GenAI).

    At its core, generative AI is using the latest advances in machine learning (ML). AI and ML have been a focus for Amazon for over 20 years from their e-commerce recommendations engine to Alexa, powered by more than 30 different ML systems to help customers billions of times each week.

    This guidebook answers six questions to help healthcare organizations in the early stages of their generative AI journey.

    Download now.

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