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Monitor embedding drift for LLMs deployed from Amazon SageMaker JumpStart

Monitor embedding drift for LLMs deployed from Amazon SageMaker JumpStart

One of the most useful application patterns for generative AI workloads is Retrieval Augmented Generation (RAG). In the RAG pattern, we find pieces of reference content related to an input prompt by performing similarity searches on embeddings. Embeddings capture the information content in bodies of text, allowing natural language processing (NLP) models to work with …

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Designing generative AI workloads for resilience

Designing generative AI workloads for resilience

Resilience plays a pivotal role in the development of any workload, and generative AI workloads are no different. There are unique considerations when engineering generative AI workloads through a resilience lens. Understanding and prioritizing resilience is crucial for generative AI workloads to meet organizational availability and business continuity requirements. In this post, we discuss the …

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Analyze security findings faster with no-code data preparation using generative AI and Amazon SageMaker Canvas

Analyze security findings faster with no-code data preparation using generative AI and Amazon SageMaker Canvas

Data is the foundation to capturing the maximum value from AI technology and solving business problems quickly. To unlock the potential of generative AI technologies, however, there’s a key prerequisite: your data needs to be appropriately prepared. In this post, we describe how use generative AI to update and scale your data pipeline using Amazon …

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Getting started with Amazon Titan Text Embeddings

Getting started with Amazon Titan Text Embeddings

Embeddings play a key role in natural language processing (NLP) and machine learning (ML). Text embedding refers to the process of transforming text into numerical representations that reside in a high-dimensional vector space. This technique is achieved through the use of ML algorithms that enable the understanding of the meaning and context of data (semantic …

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