Favorite This post is co-written with Hossein Salami and Jwalant Vyas from MSD.  In the biopharmaceutical industry, deviations in the manufacturing process are rigorously addressed. Each deviation is thoroughly documented, and its various aspects and potential impacts are closely examined to help ensure drug product quality, patient safety, and compliance.

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Shared by AWS Machine Learning November 20, 2025

Favorite Large language models (LLMs) now support a wide range of use cases, from content summarization to the ability to reason about complex tasks. One exciting new topic is taking generative AI to the physical world by applying it to robotics and physical hardware. Inspired by this, we developed a

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Shared by AWS Machine Learning November 19, 2025

Favorite Optimizing generative AI applications relies on tailoring foundation models (FMs) using techniques such as prompt engineering, RAG, continued pre-training, and fine-tuning. Efficient fine-tuning is achieved by strategically managing hardware, training time, data volume, and model quality to reduce resource demands and maximize value. Spectrum is a new approach designed

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Shared by AWS Machine Learning November 19, 2025

Favorite This post was written with Bharath Suresh and Mary Law from Snowflake. Agentic AI is a type of AI that functions autonomously, automating a broader range of tasks with minimal supervision. It combines traditional AI and generative AI capabilities to make decisions, perform tasks, and adapt to its environment

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Shared by AWS Machine Learning November 19, 2025

Favorite At Amazon, we screen customers and transactions across our global business and its subsidiaries to comply with sanctions and other global laws. Failure to comply with these laws can result in severe financial penalties and reputational harm. Amazon’s Compliance team has developed an AI-driven screening and investigations system that

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Shared by AWS Machine Learning November 19, 2025

Favorite As AI applications grow more complex, many builders struggle to appropriately and responsibly balance AI benefits and risks. Few resources exist that help non-experts articulate and resolve the key design decisions they must make. However, it doesn’t have to be this way. Today, we’re announcing the AWS Well-Architected Responsible

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Shared by AWS Machine Learning November 19, 2025

Favorite Over the past two years, I’ve worked with many customers using generative AI to transform their organizations. Most stall at experimentation, because costs stack up and timelines extend before delivering demonstrable value. A 2023 AWS MIT Chief Data Officer (CDO) Symposium survey backs this up, reporting that while 71%

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Shared by AWS Machine Learning November 18, 2025

Favorite Amazon SageMaker HyperPod is a purpose-built infrastructure for optimizing foundation model training and inference at scale. SageMaker HyperPod removes the undifferentiated heavy lifting involved in building and optimizing machine learning (ML) infrastructure for training foundation models (FMs). As AI moves towards deployment adopting to a multitude of domains and

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Shared by AWS Machine Learning November 18, 2025

Favorite The enterprise AI conversation has fundamentally shifted. We’re no longer asking “Can AI understand language?” but rather “Can AI autonomously execute complex business processes that drive real value?” According to McKinsey research, agentic AI has the potential to generate $450 billion to $650 billion in additional annual revenue by

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Shared by AWS Machine Learning November 17, 2025

Favorite AWS Professional Services set out to help organizations accelerate their cloud adoption with expert guidance and proven methodologies. Today, we’re at a pivotal moment in consulting. Just as cloud computing transformed how enterprises build technology, agentic AI is transforming how consulting services deliver value. We believe in a future

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Shared by AWS Machine Learning November 17, 2025