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Build generative AI applications quickly with Amazon Bedrock IDE in Amazon SageMaker Unified Studio

AWS Machine Learning - AI

Building generative AI applications presents significant challenges for organizations: they require specialized ML expertise, complex infrastructure management, and careful orchestration of multiple services. Building a generative AI application SageMaker Unified Studio offers tools to discover and build with generative AI.

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Supercharge your auto scaling for generative AI inference – Introducing Container Caching in SageMaker Inference

AWS Machine Learning - AI

Today at AWS re:Invent 2024, we are excited to announce the new Container Caching capability in Amazon SageMaker, which significantly reduces the time required to scale generative AI models for inference. In our tests, we’ve seen substantial improvements in scaling times for generative AI model endpoints across various frameworks.

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Evaluating RAG applications with Amazon Bedrock knowledge base evaluation

AWS Machine Learning - AI

Although automated metrics are fast and cost-effective, they can only evaluate the correctness of an AI response, without capturing other evaluation dimensions or providing explanations of why an answer is problematic. Human evaluation, although thorough, is time-consuming and expensive at scale.

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Amazon Bedrock Model Distillation: Boost function calling accuracy while reducing cost and latency

AWS Machine Learning - AI

Amazon Bedrock Model Distillation is generally available, and it addresses the fundamental challenge many organizations face when deploying generative AI : how to maintain high performance while reducing costs and latency. Evaluation metric We use abstract syntax tree (AST) to evaluate the function calling performance.

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Designing generative AI workloads for resilience

AWS Machine Learning - AI

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. Does it have the ability to replicate data to another Region for disaster recovery purposes?

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AWS empowers sales teams using generative AI solution built on Amazon Bedrock

AWS Machine Learning - AI

Our field organization includes customer-facing teams (account managers, solutions architects, specialists) and internal support functions (sales operations). Prospecting, opportunity progression, and customer engagement present exciting opportunities to utilize generative AI, using historical data, to drive efficiency and effectiveness.

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When is data too clean to be useful for enterprise AI?

CIO

Those should be looked at differently, and the quality determined differently for those,” says Kunju Kashalikar, senior director of product management at Pentaho, a wholly owned subsidiary of Hitachi Ltd. AI needs data cleaning that’s more agile, collaborative, iterative and customized for how data is being used, adds Carlsson. “If

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