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Build an AI-powered document processing platform with open source NER model and LLM on Amazon SageMaker

Flipboard

Rather than maintaining constantly running endpoints, the system creates them on demand when document processing begins and automatically stops them upon completion. This endpoint based architecture provides decoupling between the other processing, allowing independent scaling, versioning, and maintenance of each component.

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Going beyond AI assistants: Examples from Amazon.com reinventing industries with generative AI

Flipboard

The quality assurance process includes automated testing methods combining ML-, algorithm-, or LLM-based evaluations. In addition, the process employs traditional ML procedures such as named entity recognition (NER) or estimation of final confidence with regression models. The team extensively used fine-tuned SLMs.

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Unbundling the Graph in GraphRAG

O'Reilly Media

The “distance” between each pair of neighbors can be interpreted as a probability.When a question prompt arrives, run graph algorithms to traverse this probabilistic graph, then feed a ranked index of the collected chunks to LLM. One way to build a graph to use is to connect each text chunk in the vector store with its neighbors.

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Accelerate machine learning time to value with Amazon SageMaker JumpStart and PwC’s MLOps accelerator

AWS Machine Learning Blog

With organizations increasingly investing in machine learning (ML), ML adoption has become an integral part of business transformation strategies. However, implementing ML into production comes with various considerations, notably being able to navigate the world of AI safely, strategically, and responsibly.

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Moderate your Amazon IVS live stream using Amazon Rekognition

AWS Machine Learning Blog

Amazon Rekognition Content Moderation , a capability of Amazon Rekognition , automates and streamlines image and video moderation workflows without requiring machine learning (ML) experience. This process involves the utilization of both ML and non-ML algorithms. For more detailed information, refer to the GitHub repo.

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This AI newsletter is all you need (#36)

Towards AI

The technology behind GitHub’s new code search This post provides a high-level explanation of the inner workings of GitHub’s new code search and offers a glimpse into the system architecture and technical underpinnings of the product.

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Google Research, 2022 & beyond: Robotics

Google Research AI blog

We’re also progressing towards making our learning algorithms more data efficient so that we’re not relying only on scaling data collection. Further improvements are gained by utilizing a novel structured dynamical systems architecture and combining RL with trajectory optimization , supported by novel solvers.

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