Browsing: SageMaker

Voice agents, live captioning, contact center analytics, and accessibility tools all depend on real-time speech-to-text, where your application streams audio in and receives transcription back simultaneously…

Today, Amazon SageMaker AI introduces OpenAI-compatible API support for real-time inference endpoints. If you use the OpenAI SDK, LangChain, or Strands Agents, you can now invoke…

Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, share, and manage features for machine learning (ML) models. It now supports Apache Iceberg…

When you fine-tune large language models (LLMs) with Amazon SageMaker AI while using Databricks Unity Catalog, you might face unique challenges like how to maintain strict…

The EU AI Act requires organizations fine-tuning large language models (LLMs) to track computational resources measured in floating-point operations (FLOPs) to determine compliance obligations. As customers…

Training large language models requires accurate feedback signals, but traditional reinforcement learning (RL) often struggles with reward signal reliability. The quality of these signals directly influences…

As companies of various sizes adopt graphic processing units (GPU)-based machine learning (ML) training, fine-tuning and inference workloads, the demand for GPU capacity has outpaced industry-wide…

As organizations scale generative AI workloads in production, securing reliable GPU compute has become one of the most persistent operational challenges. Large language models (LLMs) and…

Today, we’re excited to announce that Amazon SageMaker AI MLflow Apps now support MLflow version 3.10, bringing enhanced capabilities for generative AI development and streamlined experiment…

Every organization has access to the same foundation models. The real competitive advantage comes from customizing them with your proprietary data and domain expertise. But getting…