Large Language Models Metadata Software Development 
AWS Machine Learning Blog
JANUARY 7, 2025
Large language models (LLMs) have demonstrated promising capabilities in machine translation (MT) tasks. Depending on the use case, they are able to compete with neural translation models such as Amazon Translate. When using the FAISS adapter, translation units are stored into a local FAISS index along with the metadata.
DECEMBER 3, 2024
Now, Syngenta is advancing further by using large language models (LLMs) and Amazon Bedrock Agents to implement Cropwise AI on AWS, marking a new era in agricultural technology. In this post, we discuss Syngenta’s journey in developing Cropwise AI.
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MARCH 7, 2025
An AWS Batch job reads these documents, chunks them into smaller slices, then creates embeddings of the text chunks using the Amazon Titan Text Embeddings model through Amazon Bedrock and stores them in an Amazon OpenSearch Service vector database. You can create a decoupled architecture with reusable components. Tarik Makota is a Sr.
AWS Machine Learning Blog
APRIL 3, 2025
Mid-market Account Manager Amazon Q, Amazon Bedrock, and other AWS services underpin this experience, enabling us to use large language models (LLMs) and knowledge bases (KBs) to generate relevant, data-driven content for APs. Its a game-changer for serving my full portfolio of accounts.
deepsense.ai
JULY 30, 2023
To start simply, you could think of LLMOps ( Large Language Model Operations) as a way to make machine learning work better in the real world over a long period of time. As previously mentioned: model training is only part of what machine learning teams deal with. What is LLMOps? Why are these elements so important?
AWS Machine Learning Blog
MARCH 20, 2025
The evaluation framework, call metadata generation, and Amazon Q in QuickSight were new components introduced from the original PCA solution. Ragas and a human-in-the-loop UI (as described in the customer blogpost with Tealium) were used to evaluate the metadata generation and individual call Q&A portions.
AWS Machine Learning Blog
MARCH 12, 2024
In this post, we discuss how Leidos worked with AWS to develop an approach to privacy-preserving large language model (LLM) inference using AWS Nitro Enclaves. t “enclave_base” Save the LLM in the EC2 Instance We are using the open-source Bloom 560m LLM for natural language processing to generate responses.
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