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Derive generative AI powered insights from Alation Cloud Services using Amazon Q Business Custom Connector

AWS Machine Learning Blog

Amazon Q Business is a fully managed generative AI-powered assistant that can answer questions, provide summaries, generate content, and securely complete tasks based on data and information in your enterprise systems. Ensure the ingested documents are added in the Sync history tab and are in the Completed status.

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Optimize hosting DeepSeek-R1 distilled models with Hugging Face TGI on Amazon SageMaker AI

AWS Machine Learning Blog

MAX_BATCH_PREFILL_TOKENS : This parameter caps the total number of tokens processed during the prefill stage across all batched requests, a phase that is both memory-intensive and compute-bound, thereby optimizing resource utilization and preventing out-of-memory errors. The best performance was observed on ml.p4dn.24xlarge 48xlarge , ml.g6e.12xlarge

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Transforming financial analysis with CreditAI on Amazon Bedrock: Octus’s journey with AWS

AWS Machine Learning Blog

Visit octus.com to learn how we deliver rigorously verified intelligence at speed and create a complete picture for professionals across the entire credit lifecycle. The Q&A handler, running on AWS Fargate, orchestrates the complete query response cycle by coordinating between services and processing responses through the LLM pipeline.

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Boosting Salesforce Einstein’s code generating model performance with Amazon SageMaker

AWS Machine Learning Blog

Einstein has a list of over 60 features, unlocked at different price points and segmented into four main categories: machine learning (ML), natural language processing (NLP), computer vision, and automatic speech recognition. This is particularly valuable given the current market shortages of high-end GPUs.

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AI-powered code suggestions and security scans in Amazon SageMaker notebooks using Amazon CodeWhisperer and Amazon CodeGuru

AWS Machine Learning Blog

To get started, complete the following steps: On the File menu, choose New and Terminal. Use CodeWhisperer in Studio After we complete the installation steps, we can use CodeWhisperer by opening a new notebook or Python file. To get started, complete the following steps: On the File menu, choose New and Terminal.

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Deploy Falcon-40B with large model inference DLCs on Amazon SageMaker

AWS Machine Learning Blog

LMI DLCs are a complete end-to-end solution for hosting LLMs like Falcon-40B. You can monitor the status of the endpoint by calling DescribeEndpoint , which will tell you when everything is complete. His expertise lies in Deep Learning in the domains of Natural Language Processing (NLP) and Computer Vision.

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Host ML models on Amazon SageMaker using Triton: CV model with PyTorch backend

AWS Machine Learning Blog

PyTorch is a machine learning (ML) framework based on the Torch library, used for applications such as computer vision and natural language processing. Set up your environment To set up your environment, complete the following steps: Launch a SageMaker notebook instance with a g5.xlarge xlarge instance.

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