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Favorite This is Part II of a two-part series from the AWS Generative AI Innovation Center. If you missed Part I, refer to Operationalizing Agentic AI Part 1: A Stakeholder’s Guide. The biggest barrier to agentic AI isn’t the technology—it’s the operating model. In Part I, we established that organizations
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Shared by AWS Machine Learning March 16, 2026
Favorite AI is moving fast, and for most of our customers, the real opportunity isn’t in experimenting with it—it’s in running AI in production where it drives meaningful business outcomes. This means building systems that run reliably, perform at scale, and meet your organization’s security and compliance requirements. Today at
Read More Shared by AWS Machine Learning March 16, 2026
Favorite This post is a collaboration between AWS, NVIDIA and Heidi. Automatic speech recognition (ASR), often called speech-to-text (STT) is becoming increasingly critical across industries like healthcare, customer service, and media production. While pre-trained models offer strong capabilities for general speech, fine-tuning for specific domains and use cases can enhance
Read More Shared by AWS Machine Learning March 14, 2026
Favorite This post shows you how to build a scalable multimodal video search system that enables natural language search across large video datasets using Amazon Nova models and Amazon OpenSearch Service. You will learn how to move beyond manual tagging and keyword-based searches to enable semantic search that captures the
Read More Shared by AWS Machine Learning March 13, 2026
Favorite Deploying AI agents safely in regulated industries is challenging. Without proper boundaries, agents that access sensitive data or execute transactions can pose significant security risks. Unlike traditional software, an AI agent chooses actions to achieve a goal by invoking tools, accessing data, and adapting its reasoning using data from
Read More Shared by AWS Machine Learning March 13, 2026
Favorite As organizations scale their generative AI workloads on Amazon Bedrock, operational visibility into inference performance and resource consumption becomes critical. Teams running latency-sensitive applications must understand how quickly models begin generating responses. Teams managing high-throughput workloads must understand how their requests consume quota so they can avoid unexpected throttling.
Read More Shared by AWS Machine Learning March 13, 2026
Favorite EAGLE is the state-of-the-art method for speculative decoding in large language model (LLM) inference, but its autoregressive drafting creates a hidden bottleneck: the more tokens that you speculate, the more sequential forward passes the drafter needs. Eventually those overhead eats into your gains. P-EAGLE removes this ceiling by generating
Read More Shared by AWS Machine Learning March 13, 2026
Favorite A new Google AI initiative aims to improve heart health outcomes for people living in remote Australian communities. View Original Source (blog.google/technology/ai/) Here.
Favorite Agentic AI isn’t a feature you turn on. It’s a shift in how work is defined, who does it, and how decisions get made. Most enterprises learn this the hard way. They launch pilots that stall the moment they hit real processes, systems, and governance. The pattern repeats: vague
Read More Shared by AWS Machine Learning March 12, 2026
Favorite Members Newsletter – March 2026 Dear OSI supporters, Every March, Open Education Week invites us to reflect on something that will feel familiar to everyone in this community: the idea that knowledge is most powerful when it is freely accessible, adaptable, and shared. It is a principle that open
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Shared by voicesofopensource March 11, 2026