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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.

AWS 110
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Announcing the First Speakers for the Virtual Agentic AI Summit in July

ODSC - Open Data Science

With a background spanning roles such as AI Growth Lead at Arize AI, Senior Product Manager for AI at Splunk, and Head of AI at Insight Data Science, Amber has played a central role in shaping GenAI product strategy and scaling AI adoption across teams. Her most recent work includes a book on Generative AI and Large Language Models.

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Build a dynamic, role-based AI agent using Amazon Bedrock inline agents

AWS Machine Learning Blog

A/B testing and experimentation Data science teams can systematically evaluate different model-tool combinations, measure performance metrics, and analyze response patterns in controlled environments. To understand how this dynamic role-based functionality works under the hood, lets examine the following system architecture diagram.

AI 94
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Build an Amazon SageMaker Model Registry approval and promotion workflow with human intervention

AWS Machine Learning Blog

Specialist Data Engineering at Merck, and Prabakaran Mathaiyan, Sr. ML Engineer at Tiger Analytics. The large machine learning (ML) model development lifecycle requires a scalable model release process similar to that of software development. The input to the training pipeline is the features dataset.

ML 130
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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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Innovating at speed: BMW’s generative AI solution for cloud incident analysis

AWS Machine Learning Blog

It requires checking many systems and teams, many of which might be failing, because theyre interdependent. Developers need to reason about the system architecture, form hypotheses, and follow the chain of components until they have located the one that is the culprit.

AWS 112
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Why AI Agents Are Reshaping AI: What You’ll Learn from ODSC East 2025

ODSC - Open Data Science

Building Multimodal AI Agents: Agentic RAG with Vision-Language Models Suman Debnath, Principal AI/ML Advocate at Amazon WebServices Learn how to create AI agents that integrate both vision and language using retrieval-augmented generation (RAG).

AI 52