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How Apoidea Group enhances visual information extraction from banking documents with multimodal models using LLaMA-Factory on Amazon SageMaker HyperPod

Amazon SageMaker, Amazon SageMaker HyperPod, Artificial Intelligence, Customer Solutions, Generative AI /

This post is co-written with Ken Tsui, Edward Tsoi and Mickey Yip from Apoidea Group. The banking industry has long struggled with the inefficiencies associated with repetitive processes such as information extraction, document review, and auditing. These tasks, which require significant human resources, slow down critical operations such as Know Your Customer (KYC) procedures, loan […]

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How Qualtrics built Socrates: An AI platform powered by Amazon SageMaker and Amazon Bedrock

Amazon SageMaker, Customer Solutions, Generative AI /

This post is co-authored by Jay Kshirsagar and Ronald Quan from Qualtrics. The content and opinions in this post are those of the third-party author and AWS is not responsible for the content or accuracy of this post. Qualtrics, founded in 2002, is a pioneering software company that has spent over two decades creating exceptional

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Customize DeepSeek-R1 671b model using Amazon SageMaker HyperPod recipes – Part 2

Amazon SageMaker, Amazon SageMaker HyperPod, Artificial Intelligence, Intermediate (200) /

This post is the second part of the DeepSeek series focusing on model customization with Amazon SageMaker HyperPod recipes (or recipes for brevity). In Part 1, we demonstrated the performance and ease of fine-tuning DeepSeek-R1 distilled models using these recipes. In this post, we use the recipes to fine-tune the original DeepSeek-R1 671b parameter model.

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Responsible AI in action: How Data Reply red teaming supports generative AI safety on AWS

Advanced (300), AI/ML, AIML, Amazon Bedrock, Amazon Machine Learning, Amazon SageMaker, Amazon SageMaker Data & AI Governance, Generative AI, Responsible AI /

Generative AI is rapidly reshaping industries worldwide, empowering businesses to deliver exceptional customer experiences, streamline processes, and push innovation at an unprecedented scale. However, amidst the excitement, critical questions around the responsible use and implementation of such powerful technology have started to emerge. Although responsible AI has been a key focus for the industry over

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InterVision accelerates AI development using AWS LLM League and Amazon SageMaker AI

Amazon Machine Learning, Amazon SageMaker, Amazon SageMaker AI, Amazon SageMaker JumpStart, Amazon SageMaker Studio, Foundational (100), Partner solutions, Technical How-to, Thought Leadership /

Cities and local governments are continuously seeking ways to enhance their non-emergency services, recognizing that intelligent, scalable contact center solutions play a crucial role in improving citizen experiences. InterVision Systems, LLC (InterVision), an AWS Premier Tier Services Partner and Amazon Connect Service Delivery Partner, has been at the forefront of this transformation, with their contact

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

AI/ML, Amazon Machine Learning, Amazon SageMaker, Amazon SageMaker AI, Artificial Intelligence, Customer Solutions, Generative AI, Natural Language Processing, Technical How-to /

Archival data in research institutions and national laboratories represents a vast repository of historical knowledge, yet much of it remains inaccessible due to factors like limited metadata and inconsistent labeling. Traditional keyword-based search mechanisms are often insufficient for locating relevant documents efficiently, requiring extensive manual review to extract meaningful insights. To address these challenges, a

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Supercharge your LLM performance with Amazon SageMaker Large Model Inference container v15

Amazon SageMaker, Artificial Intelligence, Launch /

Today, we’re excited to announce the launch of Amazon SageMaker Large Model Inference (LMI) container v15, powered by vLLM 0.8.4 with support for the vLLM V1 engine. This version now supports the latest open-source models, such as Meta’s Llama 4 models Scout and Maverick, Google’s Gemma 3, Alibaba’s Qwen, Mistral AI, DeepSeek-R, and many more.

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How Salesforce achieves high-performance model deployment with Amazon SageMaker AI

Amazon SageMaker, Amazon SageMaker AI, Artificial Intelligence, Best Practices, Customer Solutions, Experience-Based Acceleration /

This post is a joint collaboration between Salesforce and AWS and is being cross-published on both the Salesforce Engineering Blog and the AWS Machine Learning Blog. The Salesforce AI Model Serving team is working to push the boundaries of natural language processing and AI capabilities for enterprise applications. Their key focus areas include optimizing large

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Optimizing Mixtral 8x7B on Amazon SageMaker with AWS Inferentia2

AI/ML, Amazon EC2, Amazon SageMaker, AWS Inferentia, AWS Neuron, Generative AI, Technical How-to /

Organizations are constantly seeking ways to harness the power of advanced large language models (LLMs) to enable a wide range of applications such as text generation, summarizationquestion answering, and many others. As these models grow more powerful and capable, deploying them in production environments while optimizing performance and cost-efficiency becomes more challenging. Amazon Web Services

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Reduce ML training costs with Amazon SageMaker HyperPod

Amazon Machine Learning, Amazon SageMaker, Amazon SageMaker AI, Artificial Intelligence, Foundation models, Generative AI /

Training a frontier model is highly compute-intensive, requiring a distributed system of hundreds, or thousands, of accelerated instances running for several weeks or months to complete a single job. For example, pre-training the Llama 3 70B model with 15 trillion training tokens took 6.5 million H100 GPU hours. On 256 Amazon EC2 P5 instances (p5.48xlarge,

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