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Introducing Amazon Bedrock AgentCore Gateway: Transforming enterprise AI agent tool development Dhawalkumar Patel Artificial Intelligence

​[[{“value”:” To fulfill their tasks, AI Agents need access to various capabilities including tools, data stores, prompt templates, and other agents. As organizations scale their AI initiatives, they face an exponentially growing challenge of connecting each agent to multiple tools, creating an M×N integration problem… Read More »Introducing Amazon Bedrock AgentCore Gateway: Transforming enterprise AI agent tool development Dhawalkumar Patel Artificial Intelligence

Build a scalable containerized web application on AWS using the MERN stack with Amazon Q Developer – Part 1 Bill Chan Artificial Intelligence

​[[{“value”:” The MERN (MongoDB, Express, React, Node.js) stack is a popular JavaScript web development framework. The combination of technologies is well-suited for building scalable, modern web applications, especially those requiring real-time updates and dynamic user interfaces. Amazon Q Developer is a generative AI-powered assistant that… Read More »Build a scalable containerized web application on AWS using the MERN stack with Amazon Q Developer – Part 1 Bill Chan Artificial Intelligence

Optimizing Salesforce’s model endpoints with Amazon SageMaker AI inference components Rishu Aggarwal Artificial Intelligence

​[[{“value”:” 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 Platform Model Serving team is dedicated to developing and managing services that power large language… Read More »Optimizing Salesforce’s model endpoints with Amazon SageMaker AI inference components Rishu Aggarwal Artificial Intelligence

Building a RAG chat-based assistant on Amazon EKS Auto Mode and NVIDIA NIMs Riccardo Freschi Artificial Intelligence

​[[{“value”:” Chat-based assistants powered by Retrieval Augmented Generation (RAG) are transforming customer support, internal help desks, and enterprise search, by delivering fast, accurate answers grounded in your own data. With RAG, you can use a ready-to-deploy foundation model (FM) and enrich it with your own… Read More »Building a RAG chat-based assistant on Amazon EKS Auto Mode and NVIDIA NIMs Riccardo Freschi Artificial Intelligence

Introducing Amazon Bedrock AgentCore Identity: Securing agentic AI at scale Rahul Sharma Artificial Intelligence

​[[{“value”:” We’re excited to introduce Amazon Bedrock AgentCore Identity, a comprehensive identity and access management service purpose-built for AI agents. With AgentCore Identity AI, agent developers and administrators can securely access AWS resources and third-party tools such as GitHub, Salesforce, or Slack. AgentCore Identity provides… Read More »Introducing Amazon Bedrock AgentCore Identity: Securing agentic AI at scale Rahul Sharma Artificial Intelligence

Dynamic Fine-Tuning (DFT): Bridging the Generalization Gap in Supervised Fine-Tuning (SFT) for LLMs Sajjad Ansari Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Supervised Fine-Tuning (SFT) is a standard technique for adapting LLMs to new tasks by training them on expert demonstration datasets. It is valued for its simplicity and ability to develop expert-like behavior quickly, but often underperforms in generalization compared to reinforcement learning (RL). RL… Read More »Dynamic Fine-Tuning (DFT): Bridging the Generalization Gap in Supervised Fine-Tuning (SFT) for LLMs Sajjad Ansari Artificial Intelligence Category – MarkTechPost

Pitch Accent Detection Improves Pretrained Automatic Speech Recognition Apple Machine Learning Research

​We show the performance of Automatic Speech Recognition (ASR) systems that use semi-supervised speech representations can be boosted by a complimentary pitch accent detection module, by introducing a joint ASR and pitch accent detection model. The pitch accent detection component of our model achieves a… Read More »Pitch Accent Detection Improves Pretrained Automatic Speech Recognition Apple Machine Learning Research

UICoder: Finetuning Large Language Models to Generate User Interface Code through Automated Feedback Apple Machine Learning Research

​Large language models (LLMs) struggle to consistently generate UI code that compiles and produces visually relevant designs. Existing approaches to improve generation rely on expensive human feedback or distilling a proprietary model. In this paper, we explore the use of automated feedback (compilers and multi-modal… Read More »UICoder: Finetuning Large Language Models to Generate User Interface Code through Automated Feedback Apple Machine Learning Research

Optimal Corpus Aware Training for Neural Machine Translation Apple Machine Learning Research

​Corpus Aware Training (CAT) leverages valuable corpus metadata during training by injecting corpus information into each training example, and has been found effective in the literature, commonly known as the “tagging” approach. Models trained with CAT inherently learn the quality, domain and nuance between corpora… Read More »Optimal Corpus Aware Training for Neural Machine Translation Apple Machine Learning Research